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Physiologically Based Kinetic (PBK) modelling and Human Biomonitoring Data for Mixture Risk Assessment

Abstract: 

Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents or HBM values. They can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The idea of this study was to investigate the suitability and limitations in using generic PBK models to derive BE values for several compounds with the final aim to facilitate the use of HBM data in the assessment of chemical mixtures at a screening level. The focus of this study was on testing the methodology with two generic models, IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate by means of a case study the overall approach of applying PBK models with HBM data in the context of mixtures chemical risk assessment . Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated. The study illustrates uncertainties and their sources, to finally improve the accuracy of the simulations.