Risk Assessment Of Chemical Mixtures Research Articles

S01-01 Risk assessment of complex chemical mixtures – an overview of approaches and recent developments

S01-01 Risk assessment of complex chemical mixtures – an overview of approaches and recent developments

The use of effect biomarkers in chemical mixtures risk assessment – Are they still important?

Human epidemiological studies with biomarkers of effect play an invaluable role in identifying health effects with chemical exposures and in disease prevention. Effect biomarkers that measure genetic damage are potent tools to address the carcinogenic and/or mutagenic potential of chemical exposures, increasing confidence in regulatory risk assessment decision-making processes. The micronucleus (MN) test is recognized as one of the most successful and reliable assays to assess genotoxic events, which are associated with exposures that may cause cancer. To move towards the next generation risk assessment is crucial to establish bridges between standard approaches, new approach methodologies (NAMs) and tools for increase the mechanistically-based biological plausibility in human studies, such as the adverse outcome pathways (AOPs) framework. This paper aims to highlight the still active role of MN as biomarker of effect in the evolution and applicability of new methods and approaches in human risk assessment, with the positive consequence, that the new methods provide a deeper knowledge of the mechanistically-based biology of these endpoints.

Predicting the Metal Mixture Toxicity with a Toxicokinetic-Toxicodynamic Model Considering the Time-Dependent Adverse Outcome Pathways.

Chemicals mainly exist in ecosystems as mixtures, and understanding and predicting their effects are major challenges in ecotoxicology. While the adverse outcome pathway (AOP) and toxicokinetic-toxicodynamic (TK-TD) models show promise as mechanistic approaches in chemical risk assessment, there is still a lack of methodology to incorporate the AOP into a TK-TD model. Here, we describe a novel approach that integrates the AOP and TK-TD models to predict mixture toxicity using metal mixtures (specifically Cd-Cu) as a case study. We preliminarily constructed an AOP of the metal mixture through temporal transcriptome analysis together with confirmatory bioassays. The AOP revealed that prolonged exposure time activated more key events and adverse outcomes, indicating different modes of action over time. We selected a potential key event as a proxy for damage and used it as a measurable parameter to replace the theoretical parameter (scaled damage) in the TK-TD model. This refined model, which connects molecular responses to organism outcomes, effectively predicts Cd-Cu mixture toxicity over time and can be extended to other metal mixtures and even multicomponent mixtures. Overall, our results contribute to a better understanding of metal mixture toxicity and provide insights for integrating the AOP and TK-TD models to improve risk assessment for chemical mixtures.

S14-01: Regulatory risk assessment of chemical mixtures: EFSA's approach

S14-01: Regulatory risk assessment of chemical mixtures: EFSA's approach

S14-03: Full organism approaches to support the risk assessment of chemical mixtures

S14-03: Full organism approaches to support the risk assessment of chemical mixtures

S14-02: Human in vitro models to support the risk assessment of chemical mixtures: Building a new paradigm

S14-02: Human in vitro models to support the risk assessment of chemical mixtures: Building a new paradigm

OS01-10: Transcriptomics enhanced risk assessment of chemical mixtures

OS01-10: Transcriptomics enhanced risk assessment of chemical mixtures

Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept.

Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as 'communities', reflecting co-occurrence patterns of chemicals) using a network analysis approach (n = 515 individuals) on 51 chemical substances in urine. The underlying question is whether the combined body burden of multiple chemicals is of potential health concern. If so, subsequent questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this, a biomonitoring hazard index was developed by summing over hazard quotients, where each biomarker concentration was weighted (divided) by the associated HBM health-based guidance value (HBM-HBGV, HBM value or equivalent). Altogether, for 17 out of the 51 substances, health-based guidance values were available. If the hazard index was higher than 1, then the community was considered of potential health concern and should be evaluated further. Overall, seven communities were identified in the GerES V data. Of the five mixture communities where a hazard index was calculated, the highest hazard community contained N-Acetyl-S-(2-carbamoyl-ethyl)cysteine (AAMA), but this was the only biomarker for which a guidance value was available. Of the other four communities, one included the phthalate metabolites mono-isobutyl phthalate (MiBP) and mono-n-butyl phthalate (MnBP) with high hazard quotients, which led to hazard indices that exceed the value of one in 5.8% of the participants included in the GerES V study. This biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies. Future mixture risk assessment using HBM data will benefit from additional HBM health-based guidance values based on population studies. Additionally, accounting for different biomonitoring matrices would provide a wider range of exposures. Future hazard index analyses could also take a common mode of action approach, rather than the more agnostic and non-specific approach we have taken in this proof of concept.

Mixture risk assessment and human biomonitoring: Lessons learnt from HBM4EU.

Unintentional chemical mixtures that are present in the environment are of societal concern as the (environmental) chemicals contained therein, either singly or in combination, may possess properties that are hazardous (toxic) for human health. The current regulatory practice, however, is still largely based on evaluating single chemical substances one-by-one. Over the years various research efforts have delivered tools and approaches for risk assessment of chemical mixtures, but many of these were not considered sufficiently mature for regulatory implementation. This is (partly) due to mixture risk assessment (MRA) being very complex because of the large number of chemicals present in the environment. A key element in risk assessment is information on actual exposures in the population of interest. To date, information on actual personal (internal) mixture exposures is largely absent, severely limiting MRA. The use of human biomonitoring data may improve this situation. Therefore, we investigated within the European Human Biomonitoring Initiative (HBM4EU) various approaches to assess combined exposures and MRA. Based on the insights and lessons learnt in the context of the HBM4EU project, conclusions as well as recommendations for policy development regarding chemical mixtures and for further research were drafted. These conclusions and recommendations relate to both exposure and adverse health effects in humans. The recommendations were discussed with stakeholders in a workshop held in October 2021. There was considerable support and agreement with the spirit, scope and intention of the draft recommendations. Here we describe the lessons learnt on mixture risk assessment through the HBM4EU project and present the final recommendations. Overall, HBM4EU results demonstrated the potential of human biomonitoring as an instrument to obtain insight into the real-life mixtures the human population is exposed to. Also, HBM4EU results demonstrated that chemical mixtures are of public health concern. In the majority of the cases, it was possible to identify risk drivers, i.e. chemicals that contribute more strongly than others to the health risk. The novel approaches to identify co-occurrence patterns demonstrated clusters of co-occurring chemicals; chemicals in these mixture clusters are regulated independently under different legislative frameworks. Moreover, HBM4EU data and expertise can support a science-based derivation of a Mixture Assessment Factor and gauge potential impacts on the population's exposure to chemicals. While further expansion is needed on various aspects of the mixture activities carried out in the context of HBM4EU, application of available methodologies for mixture risk assessment should already be implemented to the degree possible.

Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study.

Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called "communities", within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments.

Human risk associated with exposure to mixtures of antiandrogenic chemicals evaluated using in vitro hazard and human biomonitoring data

BackgroundScientific evidence for underestimated toxicity from unintentional exposure to chemical mixtures is mounting. Yet, harmonized approaches on how to assess the actual risk of mixtures is lacking. As part of the European Joint programme ‘Human Biomonitoring for Europe’ we explored a novel methodology for mixture risk assessment of chemicals affecting male reproductive function. MethodologyWe explored a methodology for chemical mixture risk assessment based on human in vitro data combined with human exposure data, thereby circumventing the drawbacks of using hazard data from rodents and estimated exposure intake levels. Human androgen receptor (hAR) antagonism was selected as the most important molecular initiating event linked to adverse outcomes on male reproductive health. ResultsOur work identified 231 chemicals able to interfere with hAR activity. Among these were 61 finally identified as having both reliable hAR antagonist and human biomonitoring data. Calculation of risk quotients indicated that PCBs (118, 138, 157), phthalates (BBP, DBP, DIBP), benzophenone-3, PFOS, methylparaben, triclosan, some pesticides (i.e cypermethrin, β-endosulfan, methylparathion, p,p-DDE), and a PAH metabolite (1-hydroxypyrene) contributed to the mixture effect. The major chemical mixture drivers were PCB 118, BBP, PFOS, DBP, and the UV filter benzophenone-3, together contributing with 75% of the total mixture effect that was primarily driven by high exposure values. ConclusionsThis viable way forward for mixture risk assessment of chemicals has the advantages of (1) being a more comprehensive mixture risk assessment also covering data-poor chemicals, and (2) including human data only. However, the approach is subjected to uncertainties in terms of in vitro to in vivo extrapolation, it is not ready for decision making, and needs further development. Still, the results indicate a concern for adverse effects on reproductive function in highly exposed boys, especially when considering additional exposure to data-poor chemicals and chemicals acting by other mechanisms of action.

Adverse Outcome Pathway 'Footprinting': A Novel Approach to the Integration of 21st Century Toxicology Information into Chemical Mixtures Risk Assessment.

For over a decade, New Approach Methodologies (NAMs) such as structure-activity/read-across, -omics technologies, and Adverse Outcome Pathway (AOP), have been considered within regulatory communities as alternative sources of chemical and biological information potentially relevant to human health risk assessment. Integration of NAMs into applications such as chemical mixtures risk assessment has been limited due to the lack of validation of qualitative and quantitative application to adverse health outcomes in vivo, and acceptance by risk assessors. However, leveraging existent hazard and dose-response information, including NAM-based data, for mixture component chemicals across one or more levels of biological organization using novel approaches such as AOP 'footprinting' proposed herein, may significantly advance mixtures risk assessment. AOP footprinting entails the systematic stepwise profiling and comparison of all known or suspected AOPs involved in a toxicological effect at the level of key event (KE). The goal is to identify key event(s) most proximal to an adverse outcome within each AOP suspected of contributing to a given health outcome at which similarity between mixture chemicals can be confidently determined. These key events are identified as the 'footprint' for a given AOP. This work presents the general concept, and a hypothetical example application, of AOP footprinting as a key methodology for the integration of NAM data into mixtures risk assessment.

Mixture Risk Assessment of Complex Real-Life Mixtures-The PANORAMIX Project.

Humans are involuntarily exposed to hundreds of chemicals that either contaminate our environment and food or are added intentionally to our daily products. These complex mixtures of chemicals may pose a risk to human health. One of the goals of the European Union’s Green Deal and zero-pollution ambition for a toxic-free environment is to tackle the existent gaps in chemical mixture risk assessment by providing scientific grounds that support the implementation of adequate regulatory measures within the EU. We suggest dealing with this challenge by: (1) characterising ‘real-life’ chemical mixtures and determining to what extent they are transferred from the environment to humans via food and water, and from the mother to the foetus; (2) establishing a high-throughput whole-mixture-based in vitro strategy for screening of real-life complex mixtures of organic chemicals extracted from humans using integrated chemical profiling (suspect screening) together with effect-directed analysis; (3) evaluating which human blood levels of chemical mixtures might be of concern for children’s development; and (4) developing a web-based, ready-to-use interface that integrates hazard and exposure data to enable component-based mixture risk estimation. These concepts form the basis of the Green Deal project PANORAMIX, whose ultimate goal is to progress mixture risk assessment of chemicals.

Effects of consumer products chemicals ingredients and their mixtures on the estrogen receptor/androgen receptor transcriptional activation.

Unlike the environmental pollutants or industrial chemicals, the chemicals in consumer products may pose higher levels of risks, depending on how the chemicals are used in the products and how humans interact with the products. Recently, endocrine disrupting chemicals in cosmetics, personal care products, cleaners, sunscreens, and vinyl products were analytically quantified and many active chemicals including phthalates, parabens and bisphenols were detected. This indicates a wide range of exposures from common products. In this study, 35 chemicals known to be ingredients of consumer products were selected and screened for the transactivation of estrogen receptors and androgen receptors. From the results of individual chemicals, the activity of binary/ternary mixture prepared from the agonists for the ER transcription activity was measured, and compared to the predicted values obtained by the full logistic model. The measured and the predicted values were found to be very similar. This study may suggest that prediction of mixture activity by proper models would be one of the supportive tools for the risk assessment and sound regulation of chemical mixtures which have potential endocrine disrupting effects in consumer products.

Transgenerational effects of co-exposure to cadmium and carbofuran on zebrafish based on biochemical and transcriptomic analyses

The combined toxicity of heavy metals and pesticides to aquatic organisms is still largely unexplored. In this study, we investigated the combined impacts of cadmium (Cd) and carbofuran (CAR) on female zebrafish (F0 generation) and their following F1 generation. Results showed that mixtures of Cd and CAR induced acute synergistic effects on both zebrafish adults of the F0 generation and embryos of the F1 generation. Combined exposure to Cd and CAR could obviously alter the hepatic VTG level of females, and the individual exposures increased the relative mRNA levels of vtg1 and vtg2. Through maternal transmission, co-exposure of Cd and CAR caused toxicity to 4-day-old larvae of the F1 generation, evidenced by the significant changes in T4 and VTG levels, CYP450 activity, and the relative transcriptional levels of genes related to the hormone, oxidative stress, and apoptosis. These effects were also reflected by the global gene expression pattern to 7-day-old larvae of F1 generation using the transcriptomic analysis, and they could also affect energy metabolism. Our results provided a more comprehensive insight into the transgenerational toxic impacts of heavy metal and pesticide mixtures. These findings highlighted that it was highly necessary to consider transgenerational exposures in the ecological risk assessment of chemical mixtures.

Integrating Selection and Risk Assessment of Chemical Mixtures: A Novel Approach Applied to a Breast Milk Survey.

Background:One of the main challenges of modern risk assessment is to account for combined exposure to the multitude of various substances present in food and the environment.Objective:The present work proposes a methodological approach to perform chemical risk assessment of contaminant mixtures across regulatory silos regarding an extensive range of substances and to do so when comprehensive relevant data concerning the specific effects and modes of action of the mixture components are not available.Methods:We developed a complete step-by-step approach using statistical methods to prioritize substances involved in combined exposure, and we used a component-based approach to cumulate the risk using dose additivity. The most relevant toxicological end point and the associated reference point were selected from the literature to construct a toxicological threshold for each substance.Discussion:By applying the proposed method to contaminants in breast milk, we observed that among the 19 substances comprising the selected mixture, , , and arsenic were main joint contributors to the risk of neurodevelopmental and thyroid effects for infants. In addition, contributed to the thyroid effect and to the neurodevelopmental effect. Our case study on contaminants in breast milk demonstrated the importance of crossing regulatory silos when studying mixtures and the importance of identifying risk drivers to regulate the risk related to environmental contamination. Applying this method to another set of data, such as human biomonitoring or in ecotoxicology, will reinforce its relevance for risk assessment. https://doi.org/10.1289/EHP8262

Fate-directed risk assessment of chemical mixtures: a case study for cedarwood essential oil.

The environmental risk assessment of UVCBs (i.e., substances of unknown or variable composition, complex reaction products, or biological materials) is challenging due to their inherent complexity. A particular problem is that UVCBs can contain constituents with unidentified chemical structures and/or have variable composition of constituents from batch to batch. Moreover, the composition of a UVCB in the environment is not the same as that of the UVCB in a product, meaning that a risk assessment based on environmental exposure to the UVCB in a product does not represent the actual environmental risk. Here we propose an in silico fate-directed risk assessment framework for UVCBs using cedarwood oil as a case study. The framework uses Monte Carlo simulations and the mass-balance models SimpleTreat and RAIDAR to provide quantitative information on whether unidentified constituents within the physical-chemical property space of a UVCB can be the decisive factor for the environmental risk of the entire UVCB. Thereby the framework provides a robust decision tool to evaluate if a UVCB risk assessment requires additional tests or if the data on known constituents is representative for the risk of the entire UVCB. In the case of cedarwood oil, it could be shown that a risk assessment based on the known constituents (representing around 70% of the overall UVCB by weight) is representative for the environmental risk of the entire UVCB - reducing the need for additional testing and test animals.

Public health and public law issues for the toxicological risk assessment of chemical mixtures

1. Hernández AF, Tsatsakis AM. Human exposure to chemical mixtures: Challenges for the integration of toxicology with epidemiology data in risk assessment. Food Chem Toxicol. 2017;103:188-193. doi:10.1016/j.fct.2017.03.012 CrossRef Pubmed Google Scholar

Use of large datasets of measured environmental concentrations for the ecological risk assessment of chemical mixtures in Italian streams: A case study

A method to evaluate the ecological risk of chemical mixtures in water bodies is here presented. In the first phase, the approach considered routine chemical monitoring data (MEC: measured environmental concentrations) obtained from the Italian National Institute for Environmental Protection and Research, which were georeferenced to a single coordinate system for each monitoring station. The overall mixture toxicity were then evaluated for three representative aquatic organisms (algae, Daphnia, fish) using the concentration addition model to combine exposure with ecotoxicological data (from different databases). A database management system was used to facilitate the creation, organisation, and management of the large datasets of this study. The outputs were obtained as GIS-based mixture risk maps and tables (listing the toxic unit of mixtures and individual substances) useful for further analysis. The method was applied to an Italian watershed (Adda River) as a case study. In the first phase, the mixture toxicity was calculated using two scenarios: best- and worst-case; wherein the former included only those compounds that were be detected, while the latter involved also substances with concentrations below the limit of quantification. The ratio between the two scenarios indicated the range within which mixture toxicity should ideally vary. The method demonstrates that these ratios were very small when the calculated toxicity using the best case indicated a potential risk and vice versa, indicating that the worst-case scenario could not be appropriate (extremely conservative). Consequently, in the successive phase, we focused exclusively on the best-case scenario. Finally, this approach allowed the priority mixture identification (those most likely occurring in the analysed water samples), algae as the organism at the highest risk, and the substances that contributed the most to the overall mixture toxicity (terbuthylazine and s-metolachlor for algae, and chlorpyrifos and chlorpyrifos-CH3 for Daphnia and fish).

Computational modelling: A new paradigm for chemical mixtures risk assessment?

Computational modelling: A new paradigm for chemical mixtures risk assessment?