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/m(3)), but greater than blood or urine concentrations relating to the air concentration at the 1 × 10(-5) excess cancer risk (2.9 µg/m(3)). 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.

Sources of variance in baseline gene expression in the rodent liver.

The use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variation due to individual, environmental, and technical factors. Analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies has yielded useful information on baseline fluctuations in liver gene expression in the rodent. Here, studies which highlight contributions of different factors to gene expression variability in the rodent liver are discussed including a large meta-analysis of rat liver, which identified genes that vary in control animals in the absence of chemical treatment. Genes and their pathways that are the most and least variable were identified in a number of these studies. Life stage, fasting, sex, diet, circadian rhythm and liver lobe source can profoundly influence gene expression in the liver. Recognition of biological and technical factors that contribute to variability of background gene expression can help the investigator in the design of an experiment that maximizes sensitivity and reduces the influence of confounders that may lead to misinterpretation of genomic changes. The factors that contribute to variability in liver gene expression in rodents are likely analogous to those contributing to human interindividual variability in drug response and chemical toxicity. Identification of batteries of genes that are altered in a variety of background conditions could be used to predict responses to drugs and chemicals in appropriate models of the human liver.

A weight of evidence approach for selecting exposure biomarkers for biomonitoring.

CONTEXT: It is known that there are usually several biomarkers and/or medium combinations that can be applied to answer a specific exposure question. To help determine an appropriate combination for the specific question, we have developed a weight-of-evidence Framework that provides a relative appropriateness score for competing combinations. METHODS: The Framework is based on an expert assessor's evaluation of the relevance and suitability of the biomarker and medium for the question based on a set of criteria. We provide a computer based modeling tool to guide the researcher through the process. RESULTS: We present an example with six biomarkers of benzene exposure in one matrix; the six are either the most commonly used biomarkers and/or have recent widespread usage. The example clearly demonstrates the usefulness of the Framework for scoring the choices, as well as the transparency of the method that provides the basis for discussion. CONCLUSIONS: The Framework provides for the first time a method to transparently document the rationale behind selecting, from among a set of alternatives, the most scientifically supportable exposure biomarker to address a specific biomonitoring question, thus providing a reproducible account of expert opinions on the suitability of a biomarker.