Variability of biomarkers in volunteer studies: The biological component

How Many Urine Samples Are Needed to Accurately Assess Exposure to Non-Persistent Chemicals? The Biomarker Reliability Assessment Tool (BRAT) for Scientists, Research Sponsors, and Risk Managers

In epidemiologic and exposure research, biomonitoring is often used as the basis for assessing human exposure to environmental chemicals. Studies frequently rely on a single urinary measurement per participant to assess exposure to non-persistent chemicals. However, there is a growing consensus that single urine samples may be insufficient for adequately estimating exposure. The question then arises: how many samples would be needed for optimal characterization of exposure? To help researchers answer this question, we developed a tool called the Biomarker Reliability Assessment Tool (BRAT). The BRAT is based on pharmacokinetic modeling simulations, is freely available, and is designed to help researchers determine the approximate number of urine samples needed to optimize exposure assessment. The BRAT performs Monte Carlo simulations of exposure to estimate internal levels and resulting urinary concentrations in individuals from a population based on user-specified inputs (e.g., biological half-life, within- and between-person variability in exposure). The BRAT evaluates—through linear regression and quantile classification—the precision/accuracy of the estimation of internal levels depending on the number of urine samples. This tool should guide researchers towards more robust biomonitoring and improved exposure classification in epidemiologic and exposure research, which should in turn improve the translation of that research into decision-making.

Performance of a Single Layer of Clothing or Gloves to Prevent Dermal Exposure to Pesticides

The suitability, availability, and use of protective clothing are critical factors determining the actual dermal exposure (ADE) of operators and workers to pesticides. A realistic assessment of occupational exposure to pesticides requires information about the performance of protective clothing during everyday use. In this study, the performance of clothing or gloves has been investigated based on available dermal exposure data in order to provide recommendations for default protection factors that can be used in regulatory exposure assessments. Suitable dermal exposure data from available exposure databases were collated and analysed. The data that met the selection criteria for the analysis of the performance of protective clothing comprised studies in which protective clothing like cotton coveralls, cotton clothing, polyester-cotton coveralls, Sontara coveralls, Tyvek coveralls, butyl/neoprene gloves, latex/PE/vinyl/PVC gloves, or nitrile gloves were worn. Based on available potential and ADE levels, the migration of pesticides through this protective clothing was estimated. Evaluation of exposure data showed that on average only 2.3-2.6% of the pesticides present on the outside of the clothing or gloves migrated through the garments, although there was a large variation with migration up to 99%. Forearms, legs, and chest areas of the clothing tended to have the greatest migration of pesticides. Caution is needed in the selection of the appropriate protection offered protective clothing for specific situations. This study gives valuable information on the performance of protective clothing, for use in exposure assessment and for default setting in exposure modelling, taking into account the type of clothing or gloves worn. As new data become available, it may be possible to further refine the protection factors offered by different types of clothing or gloves, particularly where a common protocol has been used.

Comparison of Measurement Methods for Dermal Exposure to Hazardous Chemicals at the Workplace: The SysDEA Project

There is a principal need for more precise methodology with regard to the determination of occupational dermal exposure. The goal of the Systematic analysis of Dermal Exposure to hazardous chemical Agents at the workplace project was therefore to generate scientific knowledge to improve and standardize measurement methods for dermal exposure to chemicals at the workplace. In addition, the comparability of different measurement methods was investigated. Different methods (body sampling by means of coveralls and patches, hand sampling by means of gloves and washing, and head sampling by means of headbands and wiping) were compared. Volunteers repeatedly performed a selection of tasks under standardized conditions in test chambers to increase the reproducibility and decrease variability. The selected tasks were pouring, rolling, spraying, and handling of objects immersed in liquid formulations, as well as dumping and handling objects contaminated with powder. For the chemical analysis, the surrogate test substance Tinopal SWN was analyzed by means of a high-performance liquid chromatographic method using a fluorescence detector. Tinopal SWN was either applied as a solid product in its pure form, or as a low and high viscosity liquid containing Tinopal SWN in dissolved form. To compare the sampling methods with patches and coveralls, the exposure values as measured on the patches were extrapolated to the surface areas of the respective parts of the coverall. Based on this extrapolation approach, using the patch method resulted in somewhat higher exposure values compared to using a coverall for all exposure situations, but the differences were only statistically significant in case of the liquid exposure situations. Using gloves resulted in significantly higher exposure values compared to hand wash for handling immersed objects, rolling, and handling contaminated objects, and slightly higher (not significant) exposure values during pouring and spraying. In the same context, applying wipe sampling resulted in higher exposure values than using a headband, which was at least partly due to extrapolation of the wipe results to the surface area of the headband. No ‘golden standard’ with regard to a preferred measurement method for dermal exposure could be identified from the methods as investigated in the current study.

Barcelona Consensus on Biomarker-Based Immunosuppressive Drugs Management in Solid Organ Transplantation

With current treatment regimens, a relatively high proportion of transplant recipients experience underimmunosuppression or overimmunosuppression. Recently, several promising biomarkers have been identified for determining patient alloreactivity, which help in assessing the risk of rejection and personal response to the drug; others correlate with graft dysfunction and clinical outcome, offering a realistic opportunity for personalized immunosuppression. This consensus document aims to help tailor immunosuppression to the needs of the individual patient. It examines current knowledge on biomarkers associated with patient risk stratification and immunosuppression requirements that have been generally accepted as promising. It is based on a comprehensive review of the literature and the expert opinion of the Biomarker Working Group of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology. The quality of evidence was systematically weighted, and the strength of recommendations was rated according to the GRADE system. Three types of biomarkers are discussed: (1) those associated with the risk of rejection (alloreactivity/tolerance), (2) those reflecting individual response to immunosuppressants, and (3) those associated with graft dysfunction. Analytical aspects of biomarker measurement and novel pharmacokinetic-pharmacodynamic models accessible to the transplant community are also addressed. Conventional pharmacokinetic biomarkers may be used in combination with those discussed in this article to achieve better outcomes and improve long-term graft survival. Our group of experts has made recommendations for the most appropriate analysis of a proposed panel of preliminary biomarkers, most of which are currently under clinical evaluation in ongoing multicentre clinical trials. A section of Next Steps was also included, in which the Expert Committee is committed to sharing this knowledge with the Transplant Community in the form of triennial updates.

Derivation of biomonitoring equivalent for inorganic tin for interpreting population-level urinary biomonitoring data

Population-level biomonitoring of tin in urine has been conducted by the U.S. National Health and Nutrition Examination Survey (NHANES) and the National Nutrition and Health Study (ENNS - Étude nationale nutrition santé) in France. The general population is predominantly exposed to inorganic tin from the consumption of canned food and beverages. The National Institute for Public Health and the Environment of the Netherlands (RIVM) has established a tolerable daily intake (TDI) for chronic exposure to inorganic tin based on a NOAEL of 20 mg/kg bw per day from a 2-year feeding study in rats. Using a urinary excretion fraction (0.25%) from a controlled human study along with a TDI value of 0.2 mg/kg bw per day, a Biomonitoring Equivalent (BE) was derived for urinary tin (26 μg/g creatinine or 20 μg/L urine). The geometric mean and the 95th percentile tin urine concentrations of the general population in U.S. (0.705 and 4.5 μg/g creatinine) and France (0.51 and 2.28 μg/g creatinine) are below the BE associated with the TDI, indicating that the population exposure to inorganic tin is below the exposure guidance value of 0.2 mg/kg bw per day. Overall, the robustness of pharmacokinetic data forming the basis of the urinary BE development is medium. The availability of internal dose and kinetic data in the animal species forming the basis of the assessment could improve the overall confidence in the present assessment.

Sources of variability in biomarker concentrations

Human biomonitoring has become a primary tool for chemical exposure characterization in a wide variety of contexts: population monitoring and characterization at a national level, assessment and description of cohort exposures, and individual exposure assessments in the context of epidemiological research into potential adverse health effects of chemical exposures. The accurate use of biomonitoring as an exposure characterization tool requires understanding of factors, apart from external exposure level, that influence variation in biomarker concentrations. This review provides an overview of factors that might influence inter- and intraindividual variation in biomarker concentrations apart from external exposure magnitude. These factors include characteristics of the specific chemical of interest, characteristics of the likely route(s) and frequency of exposure, and physiological characteristics of the biomonitoring matrix (typically, blood or urine). Intraindividual variation in biomarker concentrations may be markedly affected by the relationship between the elimination half-life and the intervals between exposure events, as well as by variation in characteristics of the biomonitored media such as blood lipid content or urinary flow rate. Variation across individuals may occur due to differences in time of sampling relative to exposure events, physiological differences influencing urinary flow or creatinine excretion rates or blood characteristics, and interindividual differences in metabolic rate or other factors influencing the absorption or excretion rate of a compound. Awareness of these factors can assist researchers in improving the design and interpretation of biomonitoring studies.

Interpreting variability in population biomonitoring data: role of elimination kinetics

Biomarker concentrations in spot samples of blood and urine are implicitly interpreted as direct surrogates for long-term exposure magnitude in a variety of contexts including (1) epidemiological studies of potential health outcomes associated with general population chemical exposure, and (2) cross-sectional population biomonitoring studies. However, numerous factors in addition to exposure magnitude influence biomarker concentrations in spot samples, including temporal variation in spot samples because of elimination kinetics. The influence of half-life of elimination relative to exposure interval is examined here using simple first-order pharmacokinetic simulations of urinary concentrations in spot samples collected at random times relative to exposure events. Repeated exposures were modeled for each individual in the simulation with exposure amounts drawn from lognormal distributions with varying geometric standard deviations. Relative variation in predicted spot sample concentrations was greater than the variation in underlying dose distributions when the half-life of elimination was shorter than the interval between exposures, with the degree of relative variation increasing as the ratio of half-life to exposure interval decreased. Results of the modeling agreed well with data from a serial urine collection data set from the Centers for Disease Control. Data from previous studies examining intra-class correlation coefficients for a range of chemicals relying upon repeated sampling support the importance of considering the half-life relative to exposure frequency in design and interpretation of studies using spot samples for exposure classification and exposure estimation. The modeling and data sets presented here provide tools that can assist in interpretation of variability in cross-sectional biomonitoring studies and in design of studies utilizing biomonitoring data as markers for exposure.

Evaluation of occupational exposure: comparison of biological and environmental variabilities using physiologically based toxicokinetic modeling

PURPOSE

Few studies compare the variabilities that characterize environmental (EM) and biological monitoring (BM) data. Indeed, comparing their respective variabilities can help to identify the best strategy for evaluating occupational exposure. The objective of this study is to quantify the biological variability associated with 18 bio-indicators currently used in work environments.

METHOD

Intra-individual (BV(intra)), inter-individual (BV(inter)), and total biological variability (BV(total)) were quantified using validated physiologically based toxicokinetic (PBTK) models coupled with Monte Carlo simulations. Two environmental exposure profiles with different levels of variability were considered (GSD of 1.5 and 2.0).

RESULTS

PBTK models coupled with Monte Carlo simulations were successfully used to predict the biological variability of biological exposure indicators. The predicted values follow a lognormal distribution, characterized by GSD ranging from 1.1 to 2.3. Our results show that there is a link between biological variability and the half-life of bio-indicators, since BV(intra) and BV(total) both decrease as the biological indicator half-lives increase. BV(intra) is always lower than the variability in the air concentrations. On an individual basis, this means that the variability associated with the measurement of biological indicators is always lower than the variability characterizing airborne levels of contaminants. For a group of workers, BM is less variable than EM for bio-indicators with half-lives longer than 10-15 h.

CONCLUSION

The variability data obtained in the present study can be useful in the development of BM strategies for exposure assessment and can be used to calculate the number of samples required for guiding industrial hygienists or medical doctors in decision-making.

A generic, cross-chemical predictive PBTK model with multiple entry routes running as application in MS Excel; design of the model and comparison of predictions with experimental results

AIM

Physiologically based toxicokinetic (PBTK) models are computational tools, which simulate the absorption, distribution, metabolism, and excretion of chemicals. The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model with a high level of transparency. The model should be able to predict blood and urine concentrations of environmental chemicals and metabolites, given a certain environmental or occupational exposure scenario.

MODEL

The model refers to a reference human of 70 kg. The partition coefficients of the parent compound and its metabolites (blood:air and tissue:blood partition coefficients of 11 organs) are estimated by means of quantitative structure-property relationship, in which five easily available physicochemical properties of the compound are the independent parameters. The model gives a prediction of the fate of the compound, based on easily available chemical properties; therefore, it can be applied as a generic model applicable to multiple compounds. Three routes of uptake are considered (inhalation, dermal, and/or oral) as well as two built-in exercise levels (at rest and at light work). Dermal uptake is estimated by the use of a dermal diffusion-based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Saturable metabolism according to Michaelis-Menten kinetics can be modelled in any of 11 organs/tissues or in liver only. Renal tubular resorption is based on a built-in algorithm, dependent on the (log) octanol:water partition coefficient. Enterohepatic circulation is optional at a user-defined rate. The generic PBTK model is available as a spreadsheet application in MS Excel. The differential equations of the model are programmed in Visual Basic. Output is presented as numerical listing over time in tabular form and in graphs. The MS Excel application of the PBTK model is available as freeware.

EXPERIMENTAL

The accuracy of the model prediction is illustrated by simulating experimental observations. Published experimental inhalation and dermal exposure studies on a series of different chemicals (pyrene, N-methyl-pyrrolidone, methyl-tert-butylether, heptane, 2-butoxyethanol, and ethanol) were selected to compare the observed data with the model-simulated data. The examples show that the model-predicted concentrations in blood and/or urine after inhalation and/or transdermal uptake have an accuracy of within an order of magnitude.

CONCLUSIONS

It is advocated that this PBTK model, called IndusChemFate, is suitable for 'first tier assessments' and for early explorations of the fate of chemicals and/or metabolites in the human body. The availability of a simple model with a minimum burden of input information on the parent compound and its metabolites might be a stimulation to apply PBTK modelling more often in the field of biomonitoring and exposure science.