Prediction of blood:air and fat:air partition coefficients of volatile organic compounds for the interpretation of data in breath gas analysis

Determination of blood:air, urine:air and plasma:air partition coefficients of selected microbial volatile organic compounds

Partition coefficients (PCs) are essential parameters for understanding the toxicokinetics of chemicals in the human body since they are used in the description of different processes of absorption, distribution, and excretion in physiologically based pharmacokinetic (PBPK) models used in chemical exposure and risk assessment. The goal of this study was to determine urine:air, blood:air and plasma:air partition coefficients (PCs) of microbial volatile organic compounds (mVOCs) previously selected as having high potential as biomarkers of indoor mold exposure. To achieve this goal, the vial-equilibration technique was used, and quantification was performed using headspace gas chromatography tandem mass spectrometry (HS-GC-MS/MS) analysis. Matrix:air PCs of 19 different mVOCs have been successfully determined and their values ranged between 14 and 3586 for urine:air, 78 and 4721 for blood:air and 64 and 5604 for plasma:air PCs. Water:air PCs were also determined, and their values varied between 16 and 2210, showing a good correlation with urine:air PCs for 17 compounds of the selected mVOCs (R2 = 0.97, slope close to unity) indicating that water:air PCs below 103 may be a good surrogate for urine:air PCs. All studied mVOCs have high blood:air PCs (greater than 78) indicating strong pulmonary uptake. Due to their high blood:urine PCs, some mVOCs may be more easily measured in blood than in urine. This work is an important preliminary step toward the use of mVOCs as potential biomarkers of indoor mold exposure. The data obtained in this study will help to determine the most appropriate matrix to use in this biomonitoring approach and will eventually facilitate the development of PBPK models for these chemicals.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

The need for multicomponent gas standards for breath biomarker analysis

Exhaled breath is a non-invasive, information-rich matrix with the potential to diagnose or monitor disease, including infectious disease. Despite significant effort dedicated to biomarker identification in case control studies, very few breath tests are established in practice. In this topical review, we identify how gas standards support breath analysis today and what is needed to support further expansion and translation to practice. We examine forensic and clinical breath tests and discuss how confidence has been built through unambiguous biomarker identification and quantitation supported by gas calibration standards. Based on this discussion, we identify a need for multicomponent gas standards with part-per-trillion to part-per-million concentrations. We highlight National Institute of Standards and Technology (NIST) gas standards developed for atmospheric measurements that are also relevant to breath analysis and describe investigations of long-term stability, chemical reactions, and interactions with gas cylinder wall treatments. An overview of emerging online instruments and their need for gas standards is also presented. This review concludes with a discussion of our ongoing research to examine the feasibility of producing multicomponent gas standards at breath-relevant concentrations. Such standards could be used to investigate interference from ubiquitous endogenous compounds and as a starting point for standards tailored to specific breath tests.

Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.

Volatile compounds in human breath: critical review and meta-analysis

Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

Topical review on monitoring tetrahydrocannabinol in breath

Legalization of cannabis for recreational use has compelled governments to seek new tools to accurately monitor Δ9-tetrahydrocannabinol (Δ9-THC) and understand its effect on impairment. Various methods have been employed to measure Δ9-THC, and its respective metabolites, in different biological matrices. Recently, breath analysis has gained interest as a non-invasive method for the detection of chemicals that are either produced as part of biological processes or are absorbed from the environment. Existing breath analyzers function by analyzing previously collected samples or by direct real-time analysis. Portable hand-held devices are of particular interest for law enforcement and personal use. This paper reviews and compares both commercially available and prototype devices that proclaim Δ9-THC detection in exhaled breath using methods such as Field Asymmetric Ion Mobility Spectrometry, Semiconductor-Enriched Single-Walled Carbon Nanotube chemiresistors, Liquid Chromatography Tandem-mass Spectrometry, microfluidic-based artificial olfaction, and optical-based gas sensing.

Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry

Despite the attractiveness of breath analysis as a non-invasive means to retrieve relevant metabolic information, its introduction into routine clinical practice remains a challenge. Among all the different analytical techniques available to interrogate exhaled breath, secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) offers a number of advantages (e.g., real-time, yet wide, metabolome coverage) that makes it ideal for untargeted and targeted studies. However, so far, SESI-HRMS has relied mostly on lab-built prototypes, making it difficult to standardize breath sampling and subsequent analysis, hence preventing further developments such as multi-center clinical studies. To address this issue, we present here a number of new developments. In particular, we have characterized a new SESI interface featuring real-time readout of critical exhalation parameters such as CO₂, exhalation flow rate, and exhaled volume. Four healthy subjects provided breath specimens over a period of 1 month to characterize the stability of the SESI-HRMS system. A first assessment of the repeatability of the system using a gas standard revealed a coefficient of variation (CV) of 2.9%. Three classes of aldehydes, namely 4-hydroxy-2-alkenals, 2-alkenals and 4-hydroxy-2,6-alkedienals―hypothesized to be markers of oxidative stress―were chosen as representative metabolites of interest to evaluate the repeatability and reproducibility of this breath analysis analytical platform. Median and interquartile ranges (IQRs) of CVs for CO₂, exhalation flow rate, and exhaled volume were 3.2% (1.5%), 3.1% (1.9%), and 5.0% (4.6%), respectively. Despite the high repeatability observed for these parameters, we observed a systematic decay in the signal during repeated measurements for the shorter fatty aldehydes, which eventually reached a steady state after three/four repeated exhalations. In contrast, longer fatty aldehydes showed a steady behavior, independent of the number of repeated exhalation maneuvers. We hypothesize that this highly molecule-specific and individual-independent behavior may be explained by the fact that shorter aldehydes (with higher estimated blood-to-air partition coefficients; approaching 100) mainly get exchanged in the airways of the respiratory system, whereas the longer aldehydes (with smaller estimated blood-to-air partition coefficients; approaching 10) are thought to exchange mostly in the alveoli. Exclusion of the first three exhalations from the analysis led to a median CV (IQR) of 6.7 % (5.5 %) for the said classes of aldehydes. We found that such intra-subject variability is in general much lower than inter-subject variability (median relative differences between subjects 48.2%), suggesting that the system is suitable to capture such differences. No batch effect due to sampling date was observed, overall suggesting that the intra-subject variability measured for these series of aldehydes was biological rather than technical. High correlations found among the series of aldehydes support this notion. Finally, recommendations for breath sampling and analysis for SESI-HRMS users are provided with the aim of harmonizing procedures and improving future inter-laboratory comparisons.

The Dual Role of the Pervasive "Fattish" Tissue Remodeling With Age

Human aging is characterized by dramatic changes in body mass composition that include a general increase of the total fat mass. Within the fat mass, a change in the proportions of adipose tissues also occurs with aging, affecting body metabolism, and playing a central role in many chronic diseases, including insulin resistance, obesity, cardiovascular diseases, and type II diabetes. In mammals, fat accumulates as white (WAT) and brown (BAT) adipose tissue, which differ both in morphology and function. While WAT is involved in lipid storage and immuno-endocrine responses, BAT is aimed at generating heat. With advancing age BAT declines, while WAT increases reaching the maximum peak by early old age and changes its distribution toward a higher proportion of visceral WAT. However, lipids tend to accumulate also within lipid droplets (LDs) in non-adipose tissues, including muscle, liver, and heart. The excess of such ectopic lipid deposition and the alteration of LD homeostasis contribute to the pathogenesis of the above-mentioned age-related diseases. It is not clear why age-associated tissue remodeling seems to lean toward lipid deposition as a "default program." However, it can be noted that such remodeling is not inevitably detrimental. In fact, such a programmed redistribution of fat throughout life could be considered physiological and even protective, in particular at extreme old age. In this regard, it has to be considered that an excessive decrease of subcutaneous peripheral fat is associated with a pro-inflammatory status, and a decrease of LD is associated with lipotoxicity leading to an increased risk of insulin resistance, type II diabetes and cardiovascular diseases. At variance, a balanced rate of fat content and distribution has beneficial effects for health and metabolic homeostasis, positively affecting longevity. In this review, we will summarize the present knowledge on the mechanisms of the age-related changes in lipid distribution and we will discuss how fat mass negatively or positively impacts on human health and longevity.

Distinguishing Petroleum (Crude Oil and Fuel) From Smoke Exposure within Populations Based on the Relative Blood Levels of Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX), Styrene and 2,5-Dimethylfuran by Pattern Recognition Using Artificial Neural Networks

Studies of exposure to petroleum (crude oil/fuel) often involve monitoring benzene, toluene, ethylbenzene, xylenes (BTEX), and styrene (BTEXS) because of their toxicity and gas-phase prevalence, where exposure is typically by inhalation. However, BTEXS levels in the general U.S. population are primarily from exposure to tobacco smoke, where smokers have blood levels on average up to eight times higher than nonsmokers. This work describes a method using partition theory and artificial neural network (ANN) pattern recognition to classify exposure source based on relative BTEXS and 2,5-dimethylfuran blood levels. A method using surrogate signatures to train the ANN was validated by comparing blood levels among cigarette smokers from the National Health and Nutrition Examination Survey (NHANES) with BTEXS and 2,5-dimethylfuran signatures derived from the smoke of machine-smoked cigarettes. Classification agreement for an ANN model trained with relative VOC levels was up to 99.8% for nonsmokers and 100.0% for smokers. As such, because there is limited blood level data on individuals exposed to crude oil/fuel, only surrogate signatures derived from crude oil and fuel were used for training the ANN. For the 2007-2008 NHANES data, the ANN model assigned 7 out of 1998 specimens (0.35%) and for the 2013-2014 NHANES data 12 out of 2906 specimens (0.41%) to the crude oil/fuel signature category.

Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study

RATIONALE

Malignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell's metabolism.

OBJECTIVES

The goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose).

METHODS

Breath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose.

RESULTS

Using GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively.

CONCLUSION

This study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening.