Chemical Safety for Sustainability (CSS): Human in vivo biomonitoring data for complementing results from in vitro toxicology—A commentary

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Bayesian inference of chemical exposures from NHANES urine biomonitoring data

BACKGROUND

Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data.

OBJECTIVE

Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty.

METHODS

Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts.

RESULTS

Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015-2016 NHANES cohort.

SIGNIFICANCE

The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.

Monitoring of Air Pollution by Moss Bags around an Oil Refinery: A Critical Evaluation over 16 Years

The present study analyzes the results of a biomonitoring campaign, carried out by means of Hypnum cupressiforme Hedw. moss bags around an oil refinery, located in the southwestern part of Sardinia island (Italy). This work focuses mainly on the effects of rainfall and distance from the source of contamination on the content of 14 trace elements measured over 16 years. In addition, to point out any increasing or decreasing trends, as well as any peak in presence of airborne pollutants in the area, annual elements’ concentration values are plotted and discussed. Coefficients of variation were also calculated on accumulation values in order to evaluate stability of measurements across the years and to evaluate if similar exposure conditions, i.e., humidity and distance from contamination source, resulted in more uniform accumulation values. In conclusion, (i) the vicinity of the source of contamination as well as rainfall influenced element content in the biomonitor in the case study differently, depending on the considered element and on the exposure condition, (ii) H. cupressiforme moss bags provided relatively stable measurements during the 16-year time frame (observed variations in elements content can be attributed to environmental inputs in the area), (iii) similar conditions of exposure determined less variable accumulation values.

Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, preclinical health effects assessments, pharmacological drug development and testing, and medical diagnostics. Within this framework, the intra-class correlation coefficient (ICC) serves as an important tool for gaining insight into human variability and responses and for developing risk-based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide data for clinical and public health efforts are continually evolving to expand our knowledge base of the many thousands of environmental and biomarker chemicals that define human systems biology. These chemicals range from the smallest molecules from energy metabolism (i.e., the metabolome), through larger molecules including enzymes, proteins, RNA, DNA, and adducts. In additiona, the human body contains exogenous environmental chemicals and contributions from the microbiome from gastrointestinal, pulmonary, urogenital, naso-pharyngeal, and skin sources. This complex mixture of biomarker chemicals from environmental, human, and microbiotic sources comprise the human exposome and generally accessed through sampling of blood, breath, and urine. One of the most difficult problems in biomarker assessment is assigning probative value to any given set of measurements as there are generally insufficient data to distinguish among sources of chemicals such as environmental, microbiotic, or human metabolism and also deciding which measurements are remarkable from those that are within normal human variability. The implementation of longitudinal (repeat) measurement strategies has provided new statistical approaches for interpreting such complexities, and use of descriptive statistics based upon intra-class correlation coefficients (ICC) has become a powerful tool in these efforts. This review has two parts; the first focuses on the history of repeat measures of human biomarkers starting with occupational toxicology of the early 1950s through modern applications in interpretation of the human exposome and metabolic adverse outcome pathways (AOPs). The second part reviews different methods for calculating the ICC and explores the strategies and applications in light of different data structures.

Linking physiological parameters to perturbations in the human exposome: Environmental exposures modify blood pressure and lung function via inflammatory cytokine pathway

Human biomonitoring is an indispensable tool for evaluating the systemic effects derived from external stressors including environmental pollutants, chemicals from consumer products, and pharmaceuticals. The aim of this study was to explore consequences of environmental exposures to diesel exhaust (DE) and ozone (O₃) and ultimately to interpret these parameters from the perspective of in vitro to in vivo extrapolation. In particular, the objective was to use cytokine expression at the cellular level as a biomarker for physiological systemic responses such as blood pressure and lung function at the systemic level. The values obtained could ultimately link in vivo behavior to simpler in vitro experiments where cytokines are a measured parameter. Human exposures to combinations of DE and O₃ and the response correlations between forced exhaled volume in 1 second (FEV₁), forced vital capacity (FVC), systolic and diastolic blood pressure (SBP and DBP, respectively), and 10 inflammatory cytokines in blood (interleukins 1β, 2, 4, 5, 8, 10, 12p70 and 13, IFN-γ, and TNF-α) were determined in 15 healthy human volunteers. Results across all exposures revealed that certain individuals displayed greater inflammatory responses compared to the group and, generally, there was more between-person variation in the responses. Evidence indicates that individuals are more stable within themselves and are more likely to exhibit responses independent of one another. Data suggest that in vitro findings may ultimately be implemented to elucidate underlying adverse outcome pathways (AOP) for linking high-throughput toxicity tests to physiological in vivo responses. Further, this investigation supports assessing subjects based upon individual responses as a complement to standard longitudinal (pre vs. post) intervention grouping strategies. Ultimately, it may become possible to predict a physiological (systemic) response based upon cellular-level (in vitro) observations.

Chemical, molecular, and proteomic analyses of moss bag biomonitoring in a petrochemical area of Sardinia (Italy)

In this study, Hypnum cupressiforme moss bags were used to examine the atmospheric deposition of trace elements in the oil refinery region of Sardinia (Italy) compared with surrounding natural zones. The concentrations of 13 elements [arsenic (As), calcium (Ca), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), nickel (Ni), lead (Pb), vanadium (V), and zinc (Zn)] were determined using inductively coupled plasma optical emission spectrometry. A significant accumulation of pollutants was detected using active biomonitoring with moss bags compared with a control site. The most relevant contaminants for all of the tested sites were Cr, Cu, Ni, and Zn. Moreover, the accumulation of Cr and Zn in the refinery industrial areas, IA1 and IA2, was more than five times greater than that detected at the control site. Levels of Cd, Mg, and Pb were also higher at all of the monitored sites compared with the control site. Both genomic and proteomic methods were used to study the response of H. cupressiforme to air pollution. No DNA damage or mutations were detected using the amplified fragment length polymorphisms (AFLP) method. At the protein level, 15 gel spots exhibited differential expression profiles between the moss samples collected at the IA1 site and the control site. Furthermore, among the 14 spots that showed a decrease in protein expression, nine were associated with ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and proteins of the light-harvesting complexes of photosystem (PS) II, three were associated with protein synthesis, and three were stress-related proteins. Thus, some of these proteins may represent good moss biosensors which could be used as pre-alert markers of environmental pollution.

Taxonomic applicability of inflammatory cytokines in adverse outcome pathway (AOP) development

Cytokines, low-molecular-weight messenger proteins that act as intercellular immunomodulatory signals, have become a mainstream preclinical marker for assessing the systemic inflammatory response to external stressors. The challenge is to quantitate from healthy subjects cytokine levels that are below or at baseline and relate those dynamic and complex cytokine signatures of exposures with the inflammatory and repair pathways. Thus, highly sensitive, specific, and precise analytical and statistical methods are critically important. Investigators at the U.S. Environmental Protection Agency (EPA) have implemented advanced technologies and developed statistics for evaluating panels of inflammatory cytokines in human blood, exhaled breath condensate, urine samples, and murine biological media. Advanced multiplex, bead-based, and automated analytical platforms provided sufficient sensitivity, precision, and accuracy over the traditional enzyme-linked immunosorbent assay (ELISA). Thus, baseline cytokine levels can be quantified from healthy human subjects and animals and compared to an in vivo exposure response from an environmental chemical. Specifically, patterns of cytokine responses in humans exposed to environmental levels of ozone and diesel exhaust, and in rodents exposed to selected pesticides (such as fipronil and carbaryl), were used as case studies to generally assess the taxonomic applicability of cytokine responses. The findings in this study may aid in the application of measureable cytokine markers in future adverse outcome pathway (AOP)-based toxicity testing. Data from human and animal studies were coalesced and the possibility of using cytokines as key events (KE) to bridge species responses to external stressors in an AOP-based framework was explored.

Estimating central tendency from a single spot measure: A closed-form solution for lognormally distributed biomarker data for risk assessment at the individual level

Exposure-based risk assessment employs large cross-sectional data sets of environmental and biomarker measurements to predict population statistics for adverse health outcomes. The underlying assumption is that long-term (many years) latency health problems including cancer, autoimmune and cardiovascular disease, diabetes, and asthma are triggered by lifetime exposures to environmental stressors that interact with the genome. The aim of this study was to develop a specific predictive method that provides the statistical parameters for chronic exposure at the individual level based upon a single spot measurement and knowledge of global summary statistics as derived from large data sets. This is a profound shift in exposure and health statistics in that it begins to answer the question "How large is my personal risk?" rather than just providing an overall population-based estimate. This approach also holds value for interpreting exposure-based risks for small groups of individuals within a community in comparison to random individuals from the general population.

Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science

Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.

Probe molecule (PrM) approach in adverse outcome pathway (AOP) based high-throughput screening (HTS): in vivo discovery for developing in vitro target methods

Efficient and accurate adverse outcome pathway (AOP) based high-throughput screening (HTS) methods use a systems biology based approach to computationally model in vitro cellular and molecular data for rapid chemical prioritization; however, not all HTS assays are grounded by relevant in vivo exposure data. The challenge is to develop HTS assays with unambiguous quantitative links between in vitro responses and corresponding in vivo effects, which is complicated by metabolically insufficient systems, in vitro to in vivo extrapolation (IVIVE), cross-species comparisons, and other inherent issues correlating IVIVE findings. This article introduces the concept of ultrasensitive gas phase probe molecules (PrMs) to help bridge the current HTS assay IVIVE gap. The PrM concept assesses metabolic pathways that have already been well-defined from intact human or mammalian models. Specifically, the idea is to introduce a gas phase probe molecule into a system, observe normal steady state, add chemicals of interest, and quantitatively measure (from headspace gas) effects on PrM metabolism that can be directly linked back to a well-defined and corresponding in vivo effect. As an example, we developed the pharmacokinetic (PK) parameters and differential equations to estimate methyl tertiary butyl ether (MTBE) metabolism to tertiary butyl alcohol (TBA) via cytochrome (CYP) 2A6 in the liver from human empirical data. Because MTBE metabolic pathways are well characterized from in vivo data, we can use it as a PrM to explore direct and indirect chemical effects on CYP pathways. The PrM concept could be easily applied to in vitro and alternative models of disease and phenotype, and even test for volatile chemicals while avoiding liquid handling robotics. Furthermore, a PrM can be designed for any chemical with known empirical human exposure data and used to assess chemicals for which no information exists. Herein, we propose an elegant gas phase probe molecule-based approach to in vitro toxicity testing.

Cumulative risk assessment lessons learned: a review of case studies and issue papers

Cumulative risk assessments (CRAs) examine potential risks posed by exposure to multiple and sometimes disparate environmental stressors. CRAs are more resource intensive than single chemical assessments, and pose additional challenges and sources of uncertainty. CRAs may examine the impact of several factors on risk, including exposure magnitude and timing, chemical mixture composition, as well as physical, biological, or psychosocial stressors. CRAs are meant to increase the relevance of risk assessments, providing decision makers with information based on real world exposure scenarios that improve the characterization of actual risks and hazards. The U.S. Environmental Protection Agency has evaluated a number of CRAs, performed by or commissioned for the Agency, to seek insight into CRA concepts, methods, and lessons learned. In this article, ten case studies and five issue papers on key CRA topics are examined and a set of lessons learned are identified for CRA implementation. The lessons address the iterative nature of CRAs, importance of considering vulnerability, need for stakeholder engagement, value of a tiered approach, new methods to assess multiroute exposures to chemical mixtures, and the impact of geographical scale on approach and purpose.

Analysis of inflammatory cytokines in human blood, breath condensate, and urine using a multiplex immunoassay platform

A change in the expression of cytokines in human biological media indicates an inflammatory response to external stressors and reflects an early step along the adverse outcome pathway (AOP) for various health endpoints. To characterize and interpret this inflammatory response, methodology was developed for measuring a suite of 10 different cytokines in human blood, exhaled breath condensate (EBC), and urine using an electrochemiluminescent multiplex Th1/Th2 cytokine immunoassay platform. Measurement distributions and correlations for eight interleukins (IL) (1β, 2, 4, 5, 8, 10, 12p70 and 13), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) were evaluated using 90 blood plasma, 77 EBC, and 400 urine samples collected from nominally healthy adults subjects in North Carolina in 2008-2012. The in vivo results show that there is sufficient sensitivity for characterizing all 10 cytokines at levels of 0.05-0.10 ρg/ml with a dynamic range up to 100 ng/ml across all three of these biological media. The measured in vivo results also show that the duplicate analysis of blood, EBC and urine samples have average estimated fold ranges of 2.21, 3.49, and 2.50, respectively, which are similar to the mean estimated fold range (2.88) for the lowest concentration (0.610 ρg/ml) from a series of spiked control samples; the cytokine method can be used for all three biological media. Nine out of the 10 cytokines measured in EBC were highly correlated within one another with Spearman ρ coefficients ranging from 0.679 to 0.852, while the cytokines measured in blood had a mix of negative and positive correlations, ranging from -0.620 to 0.836. Almost all correlations between EBC and blood were positive. This work also represents the first successful within- and between-person evaluation of ultra trace-level inflammatory markers in blood, EBC, and urine.

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.