The MCRA toolbox of models and data to support chemical mixture risk assessment.

A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates.

A European model and case studies for aggregate exposure assessment of pesticides.

Exposures to plant protection products (PPPs) are assessed using risk analysis methods to protect public health. Traditionally, single sources, such as food or individual occupational sources, have been addressed. In reality, individuals can be exposed simultaneously to multiple sources. Improved regulation therefore requires the development of new tools for estimating the population distribution of exposures aggregated within an individual. A new aggregate model is described, which allows individual users to include as much, or as little, information as is available or relevant for their particular scenario. Depending on the inputs provided by the user, the outputs can range from simple deterministic values through to probabilistic analyses including characterisations of variability and uncertainty. Exposures can be calculated for multiple compounds, routes and sources of exposure. The aggregate model links to the cumulative dietary exposure model developed in parallel and is implemented in the web-based software tool MCRA. Case studies are presented to illustrate the potential of this model, with inputs drawn from existing European data sources and models. These cover exposures to UK arable spray operators, Italian vineyard spray operators, Netherlands users of a consumer spray and UK bystanders/residents. The model could also be adapted to handle non-PPP compounds.

Stakeholder attitudes towards cumulative and aggregate exposure assessment of pesticides.

This study evaluates the attitudes and perspectives of different stakeholder groups (agricultural producers, pesticide manufacturers, trading companies, retailers, regulators, food safety authorities, scientists and NGOs) towards the concepts of cumulative and aggregate exposure assessment of pesticides by means of qualitative in-depth interviews (n = 15) and a quantitative stakeholder survey (n = 65). The stakeholders involved generally agreed that the use of chemical pesticides is needed, primarily for meeting the need of feeding the growing world population, while clearly acknowledging the problematic nature of human exposure to pesticide residues. Current monitoring was generally perceived to be adequate, but the timeliness and consistency of monitoring practices across countries were questioned. The concept of cumulative exposure assessment was better understood by stakeholders than the concept of aggregate exposure assessment. Identified pitfalls were data availability, data limitations, sources and ways of dealing with uncertainties, as well as information and training needs. Regulators and food safety authorities were perceived as the stakeholder groups for whom cumulative and aggregate pesticide exposure assessment methods and tools would be most useful and acceptable. Insights obtained from this exploratory study have been integrated in the development of targeted and stakeholder-tailored dissemination and training programmes that were implemented within the EU-FP7 project ACROPOLIS.

The MCRA model for probabilistic single-compound and cumulative risk assessment of pesticides.

Pesticide risk assessment is hampered by worst-case assumptions leading to overly pessimistic assessments. On the other hand, cumulative health effects of similar pesticides are often not taken into account. This paper describes models and a web-based software system developed in the European research project ACROPOLIS. The models are appropriate for both acute and chronic exposure assessments of single compounds and of multiple compounds in cumulative assessment groups. The software system MCRA (Monte Carlo Risk Assessment) is available for stakeholders in pesticide risk assessment at mcra.rivm.nl. We describe the MCRA implementation of the methods as advised in the 2012 EFSA Guidance on probabilistic modelling, as well as more refined methods developed in the ACROPOLIS project. The emphasis is on cumulative assessments. Two approaches, sample-based and compound-based, are contrasted. It is shown that additional data on agricultural use of pesticides may give more realistic risk assessments. Examples are given of model and software validation of acute and chronic assessments, using both simulated data and comparisons against the previous release of MCRA and against the standard software DEEM-FCID used by the Environmental Protection Agency in the USA. It is shown that the EFSA Guidance pessimistic model may not always give an appropriate modelling of exposure.

Computational tool for usual intake modelling workable at the European level.

In this paper two models present in the computational tool Monte Carlo Risk Assessment (MCRA) were compared for assessing the usual intake of lead in five countries. For this, we used national food consumption data organised according to the format of the European Food Safety Authority (EFSA) Comprehensive database and a single lead concentration database in which analysed commodities were organised according to EFSA's Standard Sampling Description (SSD) system. This meant that both input data were coded according to the hierarchical FoodEx1 classification system. We demonstrate that the naïve Observed Individual Means model resulted in more conservative estimates of the exposure in the right tail of the exposure distribution compared to a refined usual intake model, the LogisticNormal­Normal model. With MCRA, the usual intake could be estimated with both models using food consumption and concentration data that were coded according to the hierarchical FoodEx1 classification system demonstrating that this tool can be used in EFSA's data environment. Additionally, the computational tool has functionalities 1) to check the input data quality by presenting detailed information about these data around a specified percentile of exposure and 2) to decide whether the use of a more refined usual intake model is appropriate.

Comparison of different exposure assessment methods to estimate the long-term dietary exposure to dioxins and ochratoxin A.

Long-term exposures to dioxins (PCCD/F and dioxin-like PCBs) and ochratoxin A were calculated using food consumption data of the European concise database combined with concentration data of the Netherlands (NL) using a deterministic approach. To refine these assessments, exposures were also calculated using three long-term exposure models, observed individual means (OIM), Iowa State University Foods (ISUF), and betabinomial-normal (BBN) models, combined with individual food consumption data of NL. BBN and ISUF correct the variation in long-term exposure for the within-person variation, whereas OIM calculates the mean exposure over the days in the food consumption survey. Exposures obtained with the concise database were highest, and those obtained with OIM higher than with BBN and ISUF. Contribution of the major sources of exposure differed between the concise database and the three models. Given the constraints of the concise database, exposures obtained with this database should be interpreted as a first tier assessment. Preferably, refined assessments using models that correct the variation in long-term exposure for the within-person variation combined with individual food consumption data and national concentration data should be used to assess the long-term exposure. We recommend the use of BBN since it can model exposure distributions that depend on covariates.

Probabilistic risk assessment of dietary exposure to single and multiple pesticide residues or contaminants: summary of the work performed within the SAFE FOODS project.

This introduction to the journal's supplement on probabilistic risk assessment of single and multiple exposure to pesticide residues or contaminants summarizes the objectives and results of the work performed in work package 3 of the EU-funded project SAFE FOODS. Within this work package, we developed an electronic platform of food consumption and chemical concentration databases harmonised at raw agricultural commodity level. In this platform the databases are connected to probabilistic software to allow probabilistic modelling of dietary exposure in a standardised way. The usefulness of this platform is demonstrated in two papers, which describe the exposure to pesticides and glycoalkaloids in several European countries. Furthermore, an integrated probabilistic risk assessment (IPRA) model was developed: a new tool to integrate exposure and effect modelling, including uncertainty analyses. The use of this model was shown in a paper on the cumulative exposure to anti-androgen pesticides. Combined with a health impact prioritization system, developed within this work package to compare heath risks between chemicals, the IPRA tool can also be used to compare health risks between multiple chemicals in complex risk assessment situation such as risk-benefit and risk trade-off analyses. Both the electronic platform of databases as the IPRA model may proof to be powerful tools to tackle the challenges risk managers are or will be faced with in the future.

A probabilistic model for simultaneous exposure to multiple compounds from food and its use for risk-benefit assessment.

A model is presented which allows to quantify the simultaneous distribution of the exposure to two compounds, for example a health-risk and a health promoting compound. The model considers the total dietary intake, and can be used as a first step to study the effects on the balance between risks and benefits following changes in the consumption pattern. The exposure is modelled separately for intake probabilities using a betabinomial model, and for intake amounts using a lognormal model, and these parts are afterwards integrated by Monte Carlo simulation. The model is illustrated using the risk-benefit case of dioxins and the omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). High concentrations of both the health adverse compounds and the health promoting compounds are simultaneously present in fatty fish. Calculated exposures were compared with intake limits: the adequate intake for EPA+DHA and the tolerable daily intake (TDI) for dioxins. We estimate the probability that dioxin exposure is below TDI, the probability that EPA+DHA exposure is above the adequate intake, and the probability that both conditions occur simultaneously. We also model the dependence of these probabilities on age. In the studied population the exposure to both compounds is almost completely below the limits. A scenario study in which meat consumption was replaced by fatty fish consumption shows an increase in the fraction of the population with the recommended intake of EPA+DHA, however also the fraction of the population exceeding the TDI for dioxins is increased. For the example scenario the optimal amount of fatty fish consumption is derived.

Risk assessment of dietary exposure to pesticides using a Bayesian method.

Risk assessment of pesticides can be a statistically difficult problem because pesticides occur only occasionally, but they may occur on multiple components in the diet. A Bayesian statistical model is presented which incorporates multivariate modelling of food consumption and modelling of pesticide measurements which are for a large part below a measurement threshold. It is shown that Bayesian modelling is feasible for a limited number of food components, and that in a data-rich situation the model compares well with an empirical Monte Carlo modelling.