Jon R. Sobus

Using liquid chromatography–tandem mass spectrometry to quantify monohydroxylated metabolites of polycyclic aromatic hydrocarbons in urine

We present an assay which employs enzyme digestion and solid phase extraction followed by liquid chromatography-tandem mass spectrometry to simultaneously quantify 16 hydroxylated polycyclic aromatic hydrocarbons (OHPAHs) in 3-ml samples of urine. The analytes consisted of 2-, 3-, and 4-ring OHPAHs, namely, 1- and 2-hydroxynaphthalene (1- and 2-OHNAP), 2-hydroxyfluorine (2-OHFLU), 1-, 2-, 3-, 4-, and 9-hydroxyphenanthrene (1-, 2-, 3-, 4-, and 9-OHPHE), 1-hydroxypyrene (1-OHPYR), 1- and 2-hydroxybenzo(a)anthracene (1- and 2-OHBAA), 3- and 6-hydroxychrysene (3- and 6-OHCHR) and 3-, 7-, and 9-hydroxybenzo(a)pyrene (3-, 7-, and 9-OHBAP). The method was validated using urine samples from steel workers and control subjects. The coefficients of variation of the method for the particular analytes were between 7% and 27% and the limits of quantitation were between 0.002 and 0.010 microg/l urine. The 2- and 3-ring OHPAHs were easily quantified in all subjects. However, 1-OHPYR was the only representative of the 4- and 5-ring metabolites that could be quantified. Pairwise correlations showed that all OHPAHs were highly correlated with each other (0.553<or=r<or=0.910) and with 1-OHPYR (0.614<or=r<or=0.910), the metabolite most widely accepted as a short-term biomarker of exposure to PAHs. The analyte, 2-OHNAP exhibited the lowest pairwise correlations with the other OHPAHs (0.542<or=r<or=0.628), presumably due to confounding by smoking. Metabolites of phenanthrene, an abundant PAH and the smallest to possess a bay region, are promising OHPAHs for characterizing both exposures to PAHs and the various metabolic pathways.

Investigation of PAH Biomarkers in the Urine of Workers Exposed to Hot Asphalt

Airborne emissions from hot asphalt contain mixtures of polycyclic aromatic hydrocarbons (PAHs), including several carcinogens. We investigated urinary biomarkers of three PAHs, namely naphthalene (Nap), phenanthrene (Phe), and pyrene (Pyr) in 20 road-paving workers exposed to hot asphalt and in 6 road milling workers who were not using hot asphalt (reference group). Our analysis included baseline urine samples as well as postshift, bedtime, and morning samples collected over three consecutive days. We measured unmetabolized Nap (U-Nap) and Phe (U-Phe) as well as the monohydroxylated metabolites of Nap (OH-Nap), Phe (OH-Phe), and Pyr (OH-Pyr) in each urine sample. In baseline samples, no significant differences in biomarker levels were observed between pavers and millers, suggesting similar background exposures. In postshift, bedtime, and morning urine samples, the high pairwise correlations observed between levels of all biomarkers suggest common exposure sources. Among pavers, levels of all biomarkers were significantly elevated in postshift samples, indicating rapid uptake and elimination of PAHs following exposure to hot asphalt (biomarker levels were not elevated among millers). Results from linear mixed-effects models of levels of U-Nap, U-Phe, OH-Phe, and OH-Pyr across pavers showed significant effects of work assignments with roller operators having lower biomarker levels than the other workers. However, no work-related effect was observed for levels of OH-Nap, apparently due to the influence of cigarette smoking. Biological half-lives, estimated from regression coefficients for time among pavers, were 8 h for U-Phe, 10 h for U-Nap, 13 h for OH-Phe and OH-Pyr, and 26 h for OH-Nap. These results support the use of U-Nap, U-Phe, OH-Phe, and OH-Pyr, but probably not OH-Nap, as short-term biomarkers of exposure to PAHs emanating from hot asphalt.

Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring

There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPAs ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPAs Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC-TOF/molecular feature data (match score≥90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.

Comparing Urinary Biomarkers of Airborne and Dermal Exposure to Polycyclic Aromatic Compounds in Asphalt-Exposed Workers

When working with hot mix asphalt, road pavers are exposed to polycyclic aromatic hydrocarbons (PAHs) through the inhalation of vapors and particulate matter (PM) and through dermal contact with PM and contaminated surfaces. Several PAHs with four to six rings are potent carcinogens which reside in these particulate emissions. Since urinary biomarkers of large PAHs are rarely detectable in asphalt workers, attention has focused upon urinary levels of the more volatile and abundant two-ring and three-ring PAHs as potential biomarkers of PAH exposure. Here, we compare levels of particulate polycyclic aromatic compounds (P-PACs, a group of aromatic hydrocarbons containing PAHs and heterocyclic compounds with four or more rings) in air and dermal patch samples from 20 road pavers to the corresponding urinary levels of naphthalene (U-Nap) (two rings), phenanthrene (U-Phe) (three rings), monohydroxylated metabolites of naphthalene (OH-Nap) and phenanthrene (OH-Phe), and 1-hydroxypyrene (OH-Pyr) (four rings), the most widely used biomarker of PAH exposure. For each worker, daily breathing-zone air (n = 55) and dermal patch samples (n = 56) were collected on three consecutive workdays along with postshift, bedtime, and morning urine samples (n = 149). Measured levels of P-PACs and the urinary analytes were used to statistically model exposure-biomarker relationships while controlling for urinary creatinine, smoking status, age, body mass index, and the timing of urine sampling. Levels of OH-Phe in urine collected postshift, at bedtime, and the following morning were all significantly associated with levels of P-PACs in air and dermal patch samples. For U-Nap, U-Phe, and OH-Pyr, both air and dermal patch measurements of P-PACs were significant predictors of postshift urine levels, and dermal patch measurements were significant predictors of bedtime urine levels (all three analytes) and morning urine levels (U-Nap and OH-Pyr only). Significant effects of creatinine concentration were observed for all analytes, and modest effects of smoking status and body mass index were observed for U-Phe and OH-Pyr, respectively. Levels of OH-Nap were not associated with P-PAC measurements in air or dermal patch samples but were significantly affected by smoking status, age, day of sample collection, and urinary creatinine. We conclude that U-Nap, U-Phe, OH-Phe, and OH-Pyr can be used as biomarkers of exposure to particulate asphalt emissions, with OH-Phe being the most promising candidate. Indications that levels of U-Nap, U-Phe, and OH-Pyr were significantly associated with dermal patch measurements well into the evening after a given work shift, combined with the small ratios of within-person variance components to between-person variance components at bedtime, suggest that bedtime measurements may be useful for investigating dermal PAH exposures.

A biomonitoring framework to support exposure and risk assessments.

BACKGROUND Biomonitoring is used in exposure and risk assessments to reduce uncertainties along the source-to-outcome continuum. Specifically, biomarkers can help identify exposure sources, routes, and distributions, and reflect kinetic and dynamic processes following exposure events. A variety of computational models now utilize biomarkers to better understand exposures at the population, individual, and sub-individual (target) levels. However, guidance is needed to clarify biomonitoring use given available measurements and models. OBJECTIVE This article presents a biomonitoring research framework designed to improve biomarker use and interpretation in support of exposure and risk assessments. DISCUSSION The biomonitoring research framework is based on a modified source-to-outcome continuum. Five tiers of biomonitoring analyses are included in the framework, beginning with simple cross-sectional and longitudinal analyses, and ending with complex analyses using various empirical and mechanistic models. Measurements and model requirements of each tier are given, as well as considerations to enhance analyses. Simple theoretical examples are also given to demonstrate applications of the framework for observational exposure studies. CONCLUSION This biomonitoring framework can be used as a guide for interpreting existing biomarker data, designing new studies to answer specific exposure- and risk-based questions, and integrating knowledge across scientific disciplines to better address human health risks.

Cumulative exposure assessment for trace-level polycyclic aromatic hydrocarbons (PAHs) using human blood and plasma analysis

Humans experience chronic cumulative trace-level exposure to mixtures of volatile, semi-volatile, and non-volatile polycyclic aromatic hydrocarbons (PAHs) present in the environment as by-products of combustion processes. Certain PAHs are known or suspected human carcinogens and so we have developed methodology for measuring their circulating (blood borne) concentrations as a tool to assess internal dose and health risk. We use liquid/liquid extraction and gas chromatography-mass spectrometry and present analytical parameters including dynamic range (0-250 ng/ml), linearity (>0.99 for all compounds), and instrument sensitivity (range 2-22 pg/ml) for a series of 22 PAHs representing 2-6-rings. The method is shown to be sufficiently sensitive for estimating PAHs baseline levels (typical median range from 1 to 1000 pg/ml) in groups of normal control subjects using 1-ml aliquots of human plasma but we note that some individuals have very low background concentrations for 5- and 6-ring compounds that fall below robust quantitation levels.

Urinary naphthalene and phenanthrene as biomarkers of occupational exposure to polycyclic aromatic hydrocarbons

Objectives: The study investigated the utility of unmetabolised naphthalene (Nap) and phenanthrene (Phe) in urine as surrogates for exposures to mixtures of polycyclic aromatic hydrocarbons (PAHs). Methods: The report included workers exposed to diesel exhausts (low PAH exposure level, n = 39) as well as those exposed to emissions from asphalt (medium PAH exposure level, n = 26) and coke ovens (high PAH exposure level, n = 28). Levels of Nap and Phe were measured in urine from each subject using head space-solid phase microextraction and gas chromatography-mass spectrometry. Published levels of airborne Nap, Phe and other PAHs in the coke-producing and aluminium industries were also investigated. Results: In post-shift urine, the highest estimated geometric mean concentrations of Nap and Phe were observed in coke-oven workers (Nap: 2490 ng/l; Phe: 975 ng/l), followed by asphalt workers (Nap: 71.5 ng/l; Phe: 54.3 ng/l), and by diesel-exposed workers (Nap: 17.7 ng/l; Phe: 3.60 ng/l). After subtracting logged background levels of Nap and Phe from the logged post-shift levels of these PAHs in urine, the resulting values (referred to as ln(adjNap) and ln(adjPhe), respectively) were significantly correlated in each group of workers (0.71⩽ Pearson r⩽0.89), suggesting a common exposure source in each case. Surprisingly, multiple linear regression analysis of ln(adjNap) on ln(adjPhe) showed no significant effect of the source of exposure (coke ovens, asphalt and diesel exhaust) and further suggested that the ratio of urinary Nap/Phe (in natural scale) decreased with increasing exposure levels. These results were corroborated with published data for airborne Nap and Phe in the coke-producing and aluminium industries. The published air measurements also indicated that Nap and Phe levels were proportional to the levels of all combined PAHs in those industries. Conclusion: Levels of Nap and Phe in urine reflect airborne exposures to these compounds and are promising surrogates for occupational exposures to PAH mixtures.

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.

Controlled human exposures to diesel exhaust

Diesel exhaust is a complex mixture of gaseous and particulate compounds resulting from an incomplete combustion of diesel fuel. Controlled human exposures to diesel exhaust and diesel exhaust particles (DEP) have contributed to understanding health effects. Such acute exposure studies of healthy subjects to diesel exhaust and DEP demonstrate a pro-inflammatory effect in the lung and systemically but only at higher concentrations (with a threshold dose approximating 300 µg/m3). Unexpectedly, there appears to be a lack of an inflammatory response to diesel exhaust and DEP in asthmatic individuals. Controlled human exposure studies of cardiovascular effects show that, comparable to other particle-associated exposures, diesel exhaust has a capacity to precipitate coronary artery disease. In addition, there is a relationship between diesel exhaust and DEP exposure and vascular endpoints; these effects in diesel exhaust may be diminished with removal of DEP. Many extra-pulmonary health effects of diesel exhaust exposure, including systemic inflammation, pro-thrombotic changes, and cardiovascular disease, are considered consequent to pro-inflammatory events and inflammation in the lung. Future research will focus on the relative importance of diesel exhaust components, potential interactions between components and other pollutants, effects in sensitive individuals, and effects of longer or repeated exposures.

Estimating Lifetime Risk from Spot Biomarker Data and Intraclass Correlation Coefficients (ICC)

Human biomarker measurements in tissues including blood, breath, and urine can serve as efficient surrogates for environmental monitoring because a single biological sample integrates personal exposure across all environmental media and uptake pathways. However, biomarkers represent a “snapshot” in time, and risk assessment is generally based on long-term averages. In this study, a statistical approach is proposed for estimating long-term average exposures from distributions of spot biomarker measurements using intraclass correlations based upon measurement variance components from the literature. This methodology was developed and demonstrated using a log-normally distributed data set of urinary OH-pyrene taken from our own studies. The calculations are generalized for any biomarker data set of spot measures such as those from the National Health and Nutrition Evaluation Studies (NHANES) requiring only spreadsheet calculations. A three-tiered approach depending on the availability of metadata was developed for converting any collection of spot biomarkers into an estimated distribution of individual means that can then be compared to a biologically relevant risk level. Examples from a Microsoft Excel-based spreadsheet for calculating estimates of the proportion of the population exceeding a given biomonitoring equivalent level are provided as an appendix.