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Application of human biomonitoring (HBM) of chemical exposure in the characterisation of health risks under REACH
International Journal of Hygiene and Environmental Health 215 (2012) 238–241
Contents lists available at SciVerse ScienceDirect
International Journal of Hygiene and Environmental Health journal homepage: www.elsevier.de/ijheh
Short communication
Application of human biomonitoring (HBM) of chemical exposure in the characterisation of health risks under REACH Peter J. Boogaard a,∗ , Lesa L. Aylward b , Sean M. Hays c a b c
Shell International BV, Shell Health, PO Box 162, 2501 AN The Hague, The Netherlands Summit Toxicology, Falls Church, VA, USA Summit Toxicology, Allenspark, CO, USA
a r t i c l e
i n f o
Keywords: Biomonitoring Equivalent DNEL Human biomonitoring Risk assessment REACH RCR
a b s t r a c t REACH requires health risk management for workers and the general population and introduced the concept of Derived No-Effect Level (DNEL). DNELs must be derived for all substances that are classified as hazardous. In analogy to other health-risk based guidance values, such as reference doses (RfDs) and tolerable daily intakes (TDIs), risk to health is considered negligible if the actual exposure is less than the DNEL. Exposure assessment is relatively simple for occupational situations but more complex for the general public, in which exposure may occur via multiple pathways, routes, and media. For such complex or partially defined exposure scenarios, human biomonitoring (HBM) gives a snapshot of the internal or absorbed dose of a chemical and is often the most reliable exposure assessment methodology. For human risk management, HBM data can be interpreted using the recently developed concept of Biomonitoring Equivalents (BEs). Basically, a BE translates an established reference value into a biomarker concentration using toxicokinetic data. If the results of an exposure assessment using HBM indicate that the levels measured are below the DNEL-based BE (BEDNEL ), it would indicate that the combined exposure via all potential exposure routes is unlikely to pose a risk to human health and that health risk management measures might not be needed. Hence, BEs do not challenge existing risk assessments but rather build upon them to help risk management, the ultimate goal of any risk assessment. A challenge in implementing this approach forms the limited availability of toxicokinetic information for many substances. However, methodologies such as generic physiologically based toxicokinetic models, which allow estimation of biomarker concentrations based on physicochemical properties, are being developed for less data-rich chemicals. Acceptance of the use of BE by regulatory authorities will allow initial screening of population exposure to chemicals to identify those chemicals requiring more detailed risk and exposure assessment, assisting in priority setting and ultimately leading to improved product stewardship and risk management. © 2011 Elsevier GmbH. All rights reserved.
Introduction The REACH regulation (EC, 2006) aims to evaluate and control human health risks for all substances in the EU. It requires producers and importers to register all substances placed on the EU market and to evaluate the risk to human health for every substance classified for toxicological hazards. Risk is a function of hazard and exposure. Once the exposure–effect relationship for an identified hazard is established, reliable data on the exposure are essential to estimate the actual risk to determine whether or not risk management measures are needed. For this process of risk characterisation, REACH introduced the concept of the Derived No-Effect Level (DNEL) which is defined as the level of a substance above
∗ Corresponding author. Tel.: +31 70 37 72 123; fax: +31 70 37 72 840. E-mail address: [email protected] (P.J. Boogaard). 1438-4639/$ – see front matter © 2011 Elsevier GmbH. All rights reserved. doi:10.1016/j.ijheh.2011.09.009
which a human should not be exposed (EC, 2006). For all potentially exposed human populations, the Risk Characterisation Ratio (RCR) must be calculated which is defined as the ratio of the estimated exposure and the DNEL. If RCR > 1, risk reduction measures are required. REACH requires risk characterisation for workers, consumers and humans liable to exposure via the environment. Exposure assessment in occupational situations is straightforward as both the exposure source and situation are normally well characterised and can be described by a specific exposure scenario (ES) which have been developed for a wide variety of industrial situations. These ESs become more complex when other exposure routes, such as dermal exposure, are involved in addition to inhalation exposure. For consumer settings the development of ESs is more complex because the variation in quantities and frequencies of use may vary largely. Finally, assessment of exposure of man via the environment is the most complex because ESs, if at all present, are
P.J. Boogaard et al. / International Journal of Hygiene and Environmental Health 215 (2012) 238–241
even less well defined and may involve oral, inhalation and dermal exposures. Human biomonitoring (HBM) is defined as the determination of a chemical or its metabolites in bodily fluids (e.g. urine, blood or saliva), tissues (e.g. hair), or exhaled air. HBM is most helpful in both the validation of ESs and the actual exposure assessment for complex ESs. HBM is usually the most reliable and accurate exposure assessment methodology because it determines integrated exposure regardless the route of exposure. Since HBM determines actual exposure (body burden) by capturing individual differences in behaviour (e.g. personal hygiene) and physiology (e.g. respiration rate), it is often also superior to other methods of exposure assessment, such as personal air measurements or dermal deposition assessments. In addition, HBM may reflect differences in metabolism and hence susceptibility. When the parameter measured is the toxic compound or proportionally related to the ultimate toxicant, HBM directly reflects the inter- and intra-individual variation and may reduce the uncertainty compared to external exposure measurements considerably (Boogaard, 2009). Although HBM is fully integrated in EU legislation, including REACH (EC, 1995, 2006; SCOEL, 1999a), guidance on how to use HBM in risk characterisation and management is limited (ECHA, 2008). This paper aims at providing practical guidance on how HBM can be applied in risk characterisation using DNELs and the recently developed concept of Biomonitoring Equivalents (BEs).
Requirements to derive a Biomonitoring Equivalent (BE) from DNELs Despite the potential advantages of HBM, the understanding of HBM data in terms of health risks is often not simple, especially when it comes to the interpretation of data obtained from consumers and the general public (Boogaard et al., 2005; Boogaard and Money, 2008). To aid in the interpretation of HBM data, the concept of Biomonitoring Equivalent (BE) was recently developed (Hays et al., 2007). BE values represent quantitative benchmarks of safe or acceptable concentrations of a chemical or its metabolite in biological specimens that are consistent with selected reference values. Examples of such reference values are acceptable or tolerable daily intake values (ADIs and TDIs), reference doses (RfDs), or minimal risk levels (MRLs) as are set by regulatory bodies such as the European Food Safety Authority (EFSA), the Joint FAO/WHO Expert Committee on Food Additives (JECFA), and the USA Environmental Protection Agency (EPA). In principle, the BE translates reference values into biomarker concentrations by integrating the risk assessment underlying the reference value with available toxicokinetic data for a compound in order to predict steady-state biomarker concentrations consistent with those reference values. Dependent on the available scientific information, this conversion can be done with varying degrees of confidence and communicated to the general public using a recently proposed framework (LaKind et al., 2008). For a variety of substances BEs have already been derived (Aylward et al., 2008a,b, 2009a; Hays and Aylward, 2008; Hays et al., 2008, 2010). Reference values, such as the ADI and RfD, represent safe exposure levels and are usually based on epidemiology and/or animal studies and have essentially the same goal as the DNEL values for consumers. Reference values have been derived and exposure data collected for a variety of chemical substances (e.g. the US EPA has published over 500 toxicity reference values in the Integrated Risk Information System). Under REACH, DNELs must be derived for all hazardous substances placed on the EU market at quantities greater than 10 tonnes/year and be compared to exposure levels to see whether the RCR is greater than 1. The requirement to calculate a RCR is estimated to apply to about 10,000 substances. To fulfil this
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task in the foreseen time scheme, new methodologies are needed. A promising approach seems to be the extension of the BE methodology by using DNELs and Occupational Exposure Limits (OELs) as reference values in combination with toxicokinetic modelling tools. There are three crucial requirements to enable the setting of a BE corresponding to the DNEL (a BEDNEL ) for a given substance. At first, there are analytical needs. There must be a reliable HBM method for the substance, which allows both the reliable collection, storage and handling of a biological specimen (e.g. blood or urine) and the subsequent analytical quantification with sufficient specificity and sensitivity. The essentials of this requirement will not be discussed here as they have been described in detail elsewhere (Calafat and Needham, 2009; Boogaard, 2009). Secondly, a DNEL or a reliable health-based OEL for the substance (vide infra) must be available to serve as the basis to derive a BEDNEL . DNELs can serve as the basis to derive a BE by applying the same methodology as was used to derive BEs from other reference values (Hays and Aylward, 2009; Hays et al., 2007; Boogaard et al., 2011). In addition, OELs as derived for industrial settings, e.g. Threshold Limit Values (TLV) and corresponding Biological Exposure Indices (BEI), and Maximum Accepted Concentrations (MAC) could be used in a similar way provided limitations and uncertainties are taken into account. Since the European Commission has already accepted that health-based OELs derived by SCOEL can be used as basis to set DNEL values, BEs can thus be derived from those values. Other health-based OELs set by expert committees could also serve as the basis to derive a BE. Although OELs, by definition, are developed to protect workers in occupational situations from adverse health effects, there is no scientific reason why OELs could not serve as the point of departure for limit values applicable to the general population using the approaches and numerical factors as provided by ECHA (2008). Thirdly, there must be sufficient basic data on the substance to allow toxicokinetic modelling or extrapolation on the basis of biomarker concentration to obtain a concentration in a biological specimen that corresponds to the DNEL or health-based OEL. This particular requirement may be a barrier for many substances since validated models are sparse. It should noted, however, that full toxicokinetic models are not essential as long as a minimum dataset is available to make a link between the reference value and the biomarker concentration (Hays and Aylward, 2008; Hays et al., 2008). For substances where specific toxicokinetic data is lacking, generalized toxicokinetic models may be used to predict the internal dose. Such models are currently being developed using more robust physicochemical data collected under the REACH regulation for every substance (Huizer et al., 2009; Bartels et al., 2009, 2010; McNally et al., submitted for publication; Jongeneelen and Ten Berge, 2011a; Loizou and Hogg, 2011).
Evaluation of HBM data using the BEDNEL values The derived BE values can be used to provide a screening level interpretation of HBM data in the context of the risk assessments. If the HBM levels for a substance are far below the BEDNEL , then the RCR will be well below 1 which is indicative for low exposures through all exposure routes. In contrast, HBM values approaching or exceeding the BEDNEL values will produce RCR values close to or greater than 1 and the substance may be prioritized for further evaluation, for instance by delineation of its exposure routes, and possible risk reduction measures. BE values have been derived for a wide variety of substances, including solvents (Aylward et al., 2008a, 2010a; Kirman et al., 2011), heavy metals (Hays et al., 2008), biocides (Aylward et al., 2010b; Krishnan et al., 2010a; Kirman et al., 2011), plasticisers (Aylward et al., 2009a), and several other chemicals and environmental pollutants (Aylward et al., 2008b; Hays and Aylward, 2008;
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Krishnan et al., 2010b) based on existing health-based reference values. BE values were also calculated for toluene, triclosan, bisphenol A and trichloroethylene based on DNEL values proving the viability of the concept to derive BEDNEL values (Boogaard et al., 2011). The substances for which these BEDNEL values were derived, all have a relatively short-life in humans ranging from a few hours to a day. Hence, the calculated BEDNEL values correspond to average concentrations for the theoretical steady exposure at the corresponding DNELs. Consequently, HBM values from a spot sample of blood or urine for an individual must be interpreted cautiously. A single measurement in an individual only provides an indication of recent rather than chronic exposure levels, and because the compound is eliminated relatively fast, levels in urine may vary substantially over the course of hours or days since exposure. However, population-based sampling can provide general information as to whether exposures are likely reaching or exceeding the DNEL on a frequent or consistent basis. It must be emphasised that also extremes in the population data must be interpreted cautiously, as they may represent fluctuations rather than typical long-term levels in any individual. However, it cannot be ruled out that extremes might represent more highly-exposed individuals; complementary exposure science and further, targeted sampling can provide additional information on the range of likely exposures in the population and help to provide additional context. A highly pragmatic use of BEDNEL values is the combination with HBM data for rapid first tier screening. For instance, the RCR for trichloroethylene, defined the ratio between the measured HBM and the BEDNEL value for trichloroethylene, was less than 0.005, far below the required ratio of less than 1. The RCR for bisphenol A (<0.01), toluene (<0.02), and triclosan (<0.03), were also well below 1 for the measured populations (Boogaard et al., 2011). Even considering the toxicokinetic uncertainties in the BEDNEL values and the HBM data, these RCR values indicate that the actual human exposure for each of the substances as reflected in the analysed HBM data sets is most likely well below the DNEL. Consequently, under REACH no risk reduction measures would be required. It should be borne in mind, however, that comparisons and conclusions from the screening evaluation are only valid for the populations represented by the HBM data sets and are only as reliable as the data sets. If specially exposed populations exist or are thought to exist, additional targeted HBM data may be required. However, it should be noted that large-scale HBM programmes like the GerES (German Environmental Survey) and NHANES (National Health and Nutrition Examination Survey) projects in Germany and the USA, respectively (GerES, 2008; Kolossa-Gehring et al., 2007; Paustenbach and Galbraith, 2006; CDC, 2011), sample several hundreds to over a thousand individuals per sampling cycle. This amount of exposure data is typically one to two orders of magnitude greater than samples available via conventional exposure measures (e.g. concentration of chemicals in air, food or water). Therefore, population based HBM programmes provide highly relevant and encompassing data.
Discussion and conclusions HBM was originally developed for workers and initially restricted to industrial settings. With the developments in analytical techniques, which allowed quantification of chemicals in biological matrices at increasingly lower levels, large populationbased HBM projects became feasible such as the GerES and NHANES projects. These projects generated extensive exposure databases on a wide variety of chemicals addressing general societal concerns about contamination of food, water and the environment. Large
population based surveys are valuable to help assess the extent of exposures among the general population. To overcome the limitations in the interpretation of data in terms of health risks, the Biomonitoring Equivalent (BE) concept was developed (Hays et al., 2007). Recently, the BE concept was extended to include DNEL and OEL values as basis to derive BE values (Boogaard et al., 2011). This approach has distinct benefits under REACH as HBM data can be interpreted quantitatively in the context of health risk assessments using the BEDNEL . If the results of an exposure assessment using HBM indicate that the levels measured are below the BEDNEL , the RCR is less than 1, indicating that the combined exposure via all potential exposure routes poses negligible health risk. Consequently, delineating exposure routes and sources and defining specific ESs would be needless. Acceptance of such use of BEDNEL values by regulatory authorities not only makes repetitious assessments for different ESs superfluous, but may also provide more accurate estimates of exposure and hence lead to improved risk management. It must be noted, however, that the BEDNEL values are only translations of the DNEL values. Thus limitations or uncertainties in the underlying DNELs and the risk assessments on which they are based should be taken into account. While the possible value of HBM data in evaluating chemical safety under REACH using BEDNEL values is apparent, there is a significant challenge since the list of chemicals of interest grows while toxicokinetic data tend to be primarily available for widely used, high volume substances and for substances of particular concern. Sophisticated PBTK models, however, are not always necessary to make a reliable prediction of a BEDNEL (Boogaard et al., 2011). Currently, a number of arithmetic toxicokinetic models and generalized PBTK models are being developed that require a minimal amount of data on a substance to predict biomonitoring values that can be used to calculate BEs (Bartels et al., 2009, 2010; Huizer et al., 2009; Jongeneelen and Ten Berge, 2011a,b; Loizou and Hogg, 2011; McNally et al., submitted for publication). The recently developed generic PBTK model IndusChemFate is available as freeware (http://www.cefic-lri.org/lri-toolbox/induschemfate). Depending on the available data, more or less sophisticated models can be used to predict the BEDNEL . The initial validations indicate that predictions made using this approach are within an order of magnitude of the actual value. This is promising as it would allow application of the calculated BEDNEL values in a tiered approach to determine whether or not there is a reason for concern. If the RCR based on HBM data and the corresponding BEDNEL is well below 1, it is unlikely that there is a realistic health risk requiring risk reduction measures. On the other hand, if the RCR would be close to or exceeding unity, a more accurate risk assessment would be triggered. In this approach the vast task of risk characterisation of about 10,000 substances over the next few years as required under REACH may be brought back to realistic proportions. In conclusion, HBM data provide significant advantages over external exposure assessments, and are being increasingly widely used to detect and assess chemical exposure in the environment and from consumer products (Sexton et al., 2004; Schulz et al., 2007; Boogaard, 2009). In parallel, there has been an increasing focus on understanding and applying mode of action, internal dose assessment, and chemical-specific assessment factors in risk assessment (Boobis et al., 2006, 2008). Coupling HBM data with a dose–response assessment based on internal and/or absorbed dose via use of a Biomonitoring Equivalent (BE) provides an extremely powerful and scientifically robust approach to conducting a risk assessment (Hays and Aylward, 2009). Conducting chemical exposure and risk assessments using both of these advances is the next logical step in this progression and provides a pragmatic tool in the context of risk management as required by REACH.
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Contents lists available at SciVerse ScienceDirect
International Journal of Hygiene and Environmental Health journal homepage: www.elsevier.de/ijheh
Short communication
Application of human biomonitoring (HBM) of chemical exposure in the characterisation of health risks under REACH Peter J. Boogaard a,∗ , Lesa L. Aylward b , Sean M. Hays c a b c
Shell International BV, Shell Health, PO Box 162, 2501 AN The Hague, The Netherlands Summit Toxicology, Falls Church, VA, USA Summit Toxicology, Allenspark, CO, USA
a r t i c l e
i n f o
Keywords: Biomonitoring Equivalent DNEL Human biomonitoring Risk assessment REACH RCR
a b s t r a c t REACH requires health risk management for workers and the general population and introduced the concept of Derived No-Effect Level (DNEL). DNELs must be derived for all substances that are classified as hazardous. In analogy to other health-risk based guidance values, such as reference doses (RfDs) and tolerable daily intakes (TDIs), risk to health is considered negligible if the actual exposure is less than the DNEL. Exposure assessment is relatively simple for occupational situations but more complex for the general public, in which exposure may occur via multiple pathways, routes, and media. For such complex or partially defined exposure scenarios, human biomonitoring (HBM) gives a snapshot of the internal or absorbed dose of a chemical and is often the most reliable exposure assessment methodology. For human risk management, HBM data can be interpreted using the recently developed concept of Biomonitoring Equivalents (BEs). Basically, a BE translates an established reference value into a biomarker concentration using toxicokinetic data. If the results of an exposure assessment using HBM indicate that the levels measured are below the DNEL-based BE (BEDNEL ), it would indicate that the combined exposure via all potential exposure routes is unlikely to pose a risk to human health and that health risk management measures might not be needed. Hence, BEs do not challenge existing risk assessments but rather build upon them to help risk management, the ultimate goal of any risk assessment. A challenge in implementing this approach forms the limited availability of toxicokinetic information for many substances. However, methodologies such as generic physiologically based toxicokinetic models, which allow estimation of biomarker concentrations based on physicochemical properties, are being developed for less data-rich chemicals. Acceptance of the use of BE by regulatory authorities will allow initial screening of population exposure to chemicals to identify those chemicals requiring more detailed risk and exposure assessment, assisting in priority setting and ultimately leading to improved product stewardship and risk management. © 2011 Elsevier GmbH. All rights reserved.
Introduction The REACH regulation (EC, 2006) aims to evaluate and control human health risks for all substances in the EU. It requires producers and importers to register all substances placed on the EU market and to evaluate the risk to human health for every substance classified for toxicological hazards. Risk is a function of hazard and exposure. Once the exposure–effect relationship for an identified hazard is established, reliable data on the exposure are essential to estimate the actual risk to determine whether or not risk management measures are needed. For this process of risk characterisation, REACH introduced the concept of the Derived No-Effect Level (DNEL) which is defined as the level of a substance above
∗ Corresponding author. Tel.: +31 70 37 72 123; fax: +31 70 37 72 840. E-mail address: [email protected] (P.J. Boogaard). 1438-4639/$ – see front matter © 2011 Elsevier GmbH. All rights reserved. doi:10.1016/j.ijheh.2011.09.009
which a human should not be exposed (EC, 2006). For all potentially exposed human populations, the Risk Characterisation Ratio (RCR) must be calculated which is defined as the ratio of the estimated exposure and the DNEL. If RCR > 1, risk reduction measures are required. REACH requires risk characterisation for workers, consumers and humans liable to exposure via the environment. Exposure assessment in occupational situations is straightforward as both the exposure source and situation are normally well characterised and can be described by a specific exposure scenario (ES) which have been developed for a wide variety of industrial situations. These ESs become more complex when other exposure routes, such as dermal exposure, are involved in addition to inhalation exposure. For consumer settings the development of ESs is more complex because the variation in quantities and frequencies of use may vary largely. Finally, assessment of exposure of man via the environment is the most complex because ESs, if at all present, are
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even less well defined and may involve oral, inhalation and dermal exposures. Human biomonitoring (HBM) is defined as the determination of a chemical or its metabolites in bodily fluids (e.g. urine, blood or saliva), tissues (e.g. hair), or exhaled air. HBM is most helpful in both the validation of ESs and the actual exposure assessment for complex ESs. HBM is usually the most reliable and accurate exposure assessment methodology because it determines integrated exposure regardless the route of exposure. Since HBM determines actual exposure (body burden) by capturing individual differences in behaviour (e.g. personal hygiene) and physiology (e.g. respiration rate), it is often also superior to other methods of exposure assessment, such as personal air measurements or dermal deposition assessments. In addition, HBM may reflect differences in metabolism and hence susceptibility. When the parameter measured is the toxic compound or proportionally related to the ultimate toxicant, HBM directly reflects the inter- and intra-individual variation and may reduce the uncertainty compared to external exposure measurements considerably (Boogaard, 2009). Although HBM is fully integrated in EU legislation, including REACH (EC, 1995, 2006; SCOEL, 1999a), guidance on how to use HBM in risk characterisation and management is limited (ECHA, 2008). This paper aims at providing practical guidance on how HBM can be applied in risk characterisation using DNELs and the recently developed concept of Biomonitoring Equivalents (BEs).
Requirements to derive a Biomonitoring Equivalent (BE) from DNELs Despite the potential advantages of HBM, the understanding of HBM data in terms of health risks is often not simple, especially when it comes to the interpretation of data obtained from consumers and the general public (Boogaard et al., 2005; Boogaard and Money, 2008). To aid in the interpretation of HBM data, the concept of Biomonitoring Equivalent (BE) was recently developed (Hays et al., 2007). BE values represent quantitative benchmarks of safe or acceptable concentrations of a chemical or its metabolite in biological specimens that are consistent with selected reference values. Examples of such reference values are acceptable or tolerable daily intake values (ADIs and TDIs), reference doses (RfDs), or minimal risk levels (MRLs) as are set by regulatory bodies such as the European Food Safety Authority (EFSA), the Joint FAO/WHO Expert Committee on Food Additives (JECFA), and the USA Environmental Protection Agency (EPA). In principle, the BE translates reference values into biomarker concentrations by integrating the risk assessment underlying the reference value with available toxicokinetic data for a compound in order to predict steady-state biomarker concentrations consistent with those reference values. Dependent on the available scientific information, this conversion can be done with varying degrees of confidence and communicated to the general public using a recently proposed framework (LaKind et al., 2008). For a variety of substances BEs have already been derived (Aylward et al., 2008a,b, 2009a; Hays and Aylward, 2008; Hays et al., 2008, 2010). Reference values, such as the ADI and RfD, represent safe exposure levels and are usually based on epidemiology and/or animal studies and have essentially the same goal as the DNEL values for consumers. Reference values have been derived and exposure data collected for a variety of chemical substances (e.g. the US EPA has published over 500 toxicity reference values in the Integrated Risk Information System). Under REACH, DNELs must be derived for all hazardous substances placed on the EU market at quantities greater than 10 tonnes/year and be compared to exposure levels to see whether the RCR is greater than 1. The requirement to calculate a RCR is estimated to apply to about 10,000 substances. To fulfil this
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task in the foreseen time scheme, new methodologies are needed. A promising approach seems to be the extension of the BE methodology by using DNELs and Occupational Exposure Limits (OELs) as reference values in combination with toxicokinetic modelling tools. There are three crucial requirements to enable the setting of a BE corresponding to the DNEL (a BEDNEL ) for a given substance. At first, there are analytical needs. There must be a reliable HBM method for the substance, which allows both the reliable collection, storage and handling of a biological specimen (e.g. blood or urine) and the subsequent analytical quantification with sufficient specificity and sensitivity. The essentials of this requirement will not be discussed here as they have been described in detail elsewhere (Calafat and Needham, 2009; Boogaard, 2009). Secondly, a DNEL or a reliable health-based OEL for the substance (vide infra) must be available to serve as the basis to derive a BEDNEL . DNELs can serve as the basis to derive a BE by applying the same methodology as was used to derive BEs from other reference values (Hays and Aylward, 2009; Hays et al., 2007; Boogaard et al., 2011). In addition, OELs as derived for industrial settings, e.g. Threshold Limit Values (TLV) and corresponding Biological Exposure Indices (BEI), and Maximum Accepted Concentrations (MAC) could be used in a similar way provided limitations and uncertainties are taken into account. Since the European Commission has already accepted that health-based OELs derived by SCOEL can be used as basis to set DNEL values, BEs can thus be derived from those values. Other health-based OELs set by expert committees could also serve as the basis to derive a BE. Although OELs, by definition, are developed to protect workers in occupational situations from adverse health effects, there is no scientific reason why OELs could not serve as the point of departure for limit values applicable to the general population using the approaches and numerical factors as provided by ECHA (2008). Thirdly, there must be sufficient basic data on the substance to allow toxicokinetic modelling or extrapolation on the basis of biomarker concentration to obtain a concentration in a biological specimen that corresponds to the DNEL or health-based OEL. This particular requirement may be a barrier for many substances since validated models are sparse. It should noted, however, that full toxicokinetic models are not essential as long as a minimum dataset is available to make a link between the reference value and the biomarker concentration (Hays and Aylward, 2008; Hays et al., 2008). For substances where specific toxicokinetic data is lacking, generalized toxicokinetic models may be used to predict the internal dose. Such models are currently being developed using more robust physicochemical data collected under the REACH regulation for every substance (Huizer et al., 2009; Bartels et al., 2009, 2010; McNally et al., submitted for publication; Jongeneelen and Ten Berge, 2011a; Loizou and Hogg, 2011).
Evaluation of HBM data using the BEDNEL values The derived BE values can be used to provide a screening level interpretation of HBM data in the context of the risk assessments. If the HBM levels for a substance are far below the BEDNEL , then the RCR will be well below 1 which is indicative for low exposures through all exposure routes. In contrast, HBM values approaching or exceeding the BEDNEL values will produce RCR values close to or greater than 1 and the substance may be prioritized for further evaluation, for instance by delineation of its exposure routes, and possible risk reduction measures. BE values have been derived for a wide variety of substances, including solvents (Aylward et al., 2008a, 2010a; Kirman et al., 2011), heavy metals (Hays et al., 2008), biocides (Aylward et al., 2010b; Krishnan et al., 2010a; Kirman et al., 2011), plasticisers (Aylward et al., 2009a), and several other chemicals and environmental pollutants (Aylward et al., 2008b; Hays and Aylward, 2008;
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Krishnan et al., 2010b) based on existing health-based reference values. BE values were also calculated for toluene, triclosan, bisphenol A and trichloroethylene based on DNEL values proving the viability of the concept to derive BEDNEL values (Boogaard et al., 2011). The substances for which these BEDNEL values were derived, all have a relatively short-life in humans ranging from a few hours to a day. Hence, the calculated BEDNEL values correspond to average concentrations for the theoretical steady exposure at the corresponding DNELs. Consequently, HBM values from a spot sample of blood or urine for an individual must be interpreted cautiously. A single measurement in an individual only provides an indication of recent rather than chronic exposure levels, and because the compound is eliminated relatively fast, levels in urine may vary substantially over the course of hours or days since exposure. However, population-based sampling can provide general information as to whether exposures are likely reaching or exceeding the DNEL on a frequent or consistent basis. It must be emphasised that also extremes in the population data must be interpreted cautiously, as they may represent fluctuations rather than typical long-term levels in any individual. However, it cannot be ruled out that extremes might represent more highly-exposed individuals; complementary exposure science and further, targeted sampling can provide additional information on the range of likely exposures in the population and help to provide additional context. A highly pragmatic use of BEDNEL values is the combination with HBM data for rapid first tier screening. For instance, the RCR for trichloroethylene, defined the ratio between the measured HBM and the BEDNEL value for trichloroethylene, was less than 0.005, far below the required ratio of less than 1. The RCR for bisphenol A (<0.01), toluene (<0.02), and triclosan (<0.03), were also well below 1 for the measured populations (Boogaard et al., 2011). Even considering the toxicokinetic uncertainties in the BEDNEL values and the HBM data, these RCR values indicate that the actual human exposure for each of the substances as reflected in the analysed HBM data sets is most likely well below the DNEL. Consequently, under REACH no risk reduction measures would be required. It should be borne in mind, however, that comparisons and conclusions from the screening evaluation are only valid for the populations represented by the HBM data sets and are only as reliable as the data sets. If specially exposed populations exist or are thought to exist, additional targeted HBM data may be required. However, it should be noted that large-scale HBM programmes like the GerES (German Environmental Survey) and NHANES (National Health and Nutrition Examination Survey) projects in Germany and the USA, respectively (GerES, 2008; Kolossa-Gehring et al., 2007; Paustenbach and Galbraith, 2006; CDC, 2011), sample several hundreds to over a thousand individuals per sampling cycle. This amount of exposure data is typically one to two orders of magnitude greater than samples available via conventional exposure measures (e.g. concentration of chemicals in air, food or water). Therefore, population based HBM programmes provide highly relevant and encompassing data.
Discussion and conclusions HBM was originally developed for workers and initially restricted to industrial settings. With the developments in analytical techniques, which allowed quantification of chemicals in biological matrices at increasingly lower levels, large populationbased HBM projects became feasible such as the GerES and NHANES projects. These projects generated extensive exposure databases on a wide variety of chemicals addressing general societal concerns about contamination of food, water and the environment. Large
population based surveys are valuable to help assess the extent of exposures among the general population. To overcome the limitations in the interpretation of data in terms of health risks, the Biomonitoring Equivalent (BE) concept was developed (Hays et al., 2007). Recently, the BE concept was extended to include DNEL and OEL values as basis to derive BE values (Boogaard et al., 2011). This approach has distinct benefits under REACH as HBM data can be interpreted quantitatively in the context of health risk assessments using the BEDNEL . If the results of an exposure assessment using HBM indicate that the levels measured are below the BEDNEL , the RCR is less than 1, indicating that the combined exposure via all potential exposure routes poses negligible health risk. Consequently, delineating exposure routes and sources and defining specific ESs would be needless. Acceptance of such use of BEDNEL values by regulatory authorities not only makes repetitious assessments for different ESs superfluous, but may also provide more accurate estimates of exposure and hence lead to improved risk management. It must be noted, however, that the BEDNEL values are only translations of the DNEL values. Thus limitations or uncertainties in the underlying DNELs and the risk assessments on which they are based should be taken into account. While the possible value of HBM data in evaluating chemical safety under REACH using BEDNEL values is apparent, there is a significant challenge since the list of chemicals of interest grows while toxicokinetic data tend to be primarily available for widely used, high volume substances and for substances of particular concern. Sophisticated PBTK models, however, are not always necessary to make a reliable prediction of a BEDNEL (Boogaard et al., 2011). Currently, a number of arithmetic toxicokinetic models and generalized PBTK models are being developed that require a minimal amount of data on a substance to predict biomonitoring values that can be used to calculate BEs (Bartels et al., 2009, 2010; Huizer et al., 2009; Jongeneelen and Ten Berge, 2011a,b; Loizou and Hogg, 2011; McNally et al., submitted for publication). The recently developed generic PBTK model IndusChemFate is available as freeware (http://www.cefic-lri.org/lri-toolbox/induschemfate). Depending on the available data, more or less sophisticated models can be used to predict the BEDNEL . The initial validations indicate that predictions made using this approach are within an order of magnitude of the actual value. This is promising as it would allow application of the calculated BEDNEL values in a tiered approach to determine whether or not there is a reason for concern. If the RCR based on HBM data and the corresponding BEDNEL is well below 1, it is unlikely that there is a realistic health risk requiring risk reduction measures. On the other hand, if the RCR would be close to or exceeding unity, a more accurate risk assessment would be triggered. In this approach the vast task of risk characterisation of about 10,000 substances over the next few years as required under REACH may be brought back to realistic proportions. In conclusion, HBM data provide significant advantages over external exposure assessments, and are being increasingly widely used to detect and assess chemical exposure in the environment and from consumer products (Sexton et al., 2004; Schulz et al., 2007; Boogaard, 2009). In parallel, there has been an increasing focus on understanding and applying mode of action, internal dose assessment, and chemical-specific assessment factors in risk assessment (Boobis et al., 2006, 2008). Coupling HBM data with a dose–response assessment based on internal and/or absorbed dose via use of a Biomonitoring Equivalent (BE) provides an extremely powerful and scientifically robust approach to conducting a risk assessment (Hays and Aylward, 2009). Conducting chemical exposure and risk assessments using both of these advances is the next logical step in this progression and provides a pragmatic tool in the context of risk management as required by REACH.
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