Risk Assessment Of Mixtures Research Articles

Risk Characterization and Probabilistic Concentration-Response Modeling of Complex Environmental Mixtures Using New Approach Methodologies (NAMs) Data from Organotypic in Vitro Human Stem Cell Assays.

Background:Risk assessment of chemical mixtures or complex substances remains a major methodological challenge due to lack of available hazard or exposure data. Therefore, risk assessors usually infer hazard or risk from data on the subset of constituents with available toxicity values.Objectives:We evaluated the validity of the widely used traditional mixtures risk assessment paradigms, Independent Action (IA) and Concentration Addition (CA), with new approach methodologies (NAMs) data from human cell-based in vitro assays.Methods:A diverse set of 42 chemicals was tested both individually and as mixtures for functional and cytotoxic effects in vitro. A panel of induced pluripotent stem cell (iPSCs)-derived models (hepatocytes, cardiomyocytes, endothelial, and neurons) and one primary cell type (HUVEC) were used. Bayesian concentration–response modeling of individual chemicals or their mixtures was performed for a total of 47 phenotypes to derive point-of-departure (POD) values. Probabilistic IA or CA was conducted to estimate the mixture effects based on the bioactivity profiles from the individual chemicals and compared with mixture bioactivity.Results:All mixtures showed significant bioactivity, even though some were constructed using individual chemical concentrations considered “low” or “safe.” Even though CA is much more accurate as a predictor of mixture effects in comparison with IA, with CA-based POD typically within an order of magnitude of the actual mixture, in some cases, the bioactivity of the mixtures appeared to be much greater than that of their components under either additivity assumption.Discussion:These results suggest that CA is a preferred first approximation for predicting mixture toxicity when data for all constituents are available. However, because the accuracy of additivity assumptions varies greatly across phenotypes, we posit that mixtures and complex substances need to be directly tested for their hazard potential. NAMs provide a practical solution that rapidly yields highly informative data for mixtures risk assessment. https://doi.org/10.1289/EHP7600

Impact of mono-culture vs. Co-culture of keratinocytes and monocytes on cytokine responses induced by important skin sensitizers

Sensitization to a contact allergen brings with it a lifelong risk to develop allergic contact dermatitis. Inflammation is an important part of the skin sensitizing mechanism, and understanding how different haptens stimulate the immune system, as well as the role played by different cell types present in skin, may be helpful for developing optimized in vitro models for risk assessment of new chemicals or mixtures. The aim of this study was to compare the cytokine profile following exposure of cells representing keratinocytes (HaCaT), monocytes (THP-1) and a co-culture of these cells to three clinically important skin sensitizers: cobalt (II) chloride (CoCl2), methylisothiazolinone (MI) and p-phenylenediamine (PPD). Secretion of ten pro-inflammatory cytokines was measured using multiplexing. The results showed that the cytokine response differed substantially between the three cell assays. CoCl2 caused an increase of IL-8 in HaCaT cells, while the induction of also IL-13 and IL-1β was observed in THP-1 cells and co-cultures. MI induced six cytokines in HaCaT cells but only IL-1β in the THP-1 cells and four cytokines in the co-culture. Interestingly, the IL-1β response was massive in the co-culture. PPD caused release of IL-1β in all three models as well as IL-8 in the co-culture. Control experiments with two non-sensitizers and irritants (lactic acid and sodium dodecyl sulfate) showed no effect on IL-8 or IL-1β in the co-culture. Taken together, results from this exploratory analysis show unique cytokine profiles dependent on the type of hapten and cell model. Importantly, all three haptens triggered secretion of IL-1β and IL-8 in a co-culture of HaCaT cells and THP-1 cells, representing the most robust test system.

Use of Effect Biomarkers for Regulatory Risk Assessment of Chemical Mixtures

General and workers populations are exposed to a wide range of chemicals in their daily lives. Exposures to these chemical mixtures depend on the different environments, routes of exposures (inhalation, skin, and ingestion), and sources. Human Biomonitoring (HBM) is a powerful tool for assessing the uptake of chemicals into the human body in a holistic manner. In this context, effect biomarkers have become an essential part of biomonitoring programs because they reveal adverse effects from chemical exposures also accounting for unknown mixtures effects, the most common human exposure scenario. Effect biomarkers enable us to link chemical exposures to their combined health effects and disease development. This effect biomarker approach implies a needed paradigm shift in regulatory risk assessment from investigating one chemical substance at the time to exposures to chemical mixtures. To date, several relevant effect biomarkers have been validated and some effect biomarkers have been implemented as part of Adverse Outcome Pathway (AOP). The previous multidisciplinary review performed represents a basis for enhancing the identification of relevant effect biomarkers and their mechanistic pathways following the AOP framework. This approach offers a systematic understanding and enables us to bring the use of effect biomarkers and chemical mixture risk assessment into regulatory use by defining mixture thresholds. Guiding principles and examples for defining AOP related effect thresholds for relevant Mode of Actions (MoAs) will be elaborated in an OECD follow up activity with the expert’s engagement from more than 25 institutes and organizations.

Combined toxicity assessment of myclobutanil and thiamethoxam to zebrafish embryos employing multi-endpoints

It is necessary to understand the interactions between different pesticides in ecotoxicology because pesticides never appear as individual compounds but rather in combinations with other compounds. In this study, we planned to explicate the combined toxic effect of myclobutanil (MYC) and thiamethoxam (THI) on the zebrafish (Danio rerio) by adopting multiple biomarkers. Results unraveled that the 96-h LC50 values of MYC to D.rerio at various life phases ranged from 5.2 to 10.3mgL-1, which were lower than those of THI ranging from 147 to 246mgL-1. Combinations of MYC and THI exhibited synergetic toxicity to zebrafish embryos. The activities of antioxidative enzymes (T-SOD, Cu/Zn-SOD and POD) and detoxification enzyme (GST) were obviously varied in most of the MYC, THI and combined exposures compared to the control. The mRNA expressions of eight genes (Cu-sod, cas3, il-8, cxcl, erα, crh, cyp17 and dio1) involved in antioxidation, apoptosis, immunity and endocrine were obviously altered in the combined exposure of MYC and THI compared to their individual exposures. Our findings hinted the threats when YMC and THI co-existed, which would be beneficial for the risk assessments of pesticide mixtures.

A common adverse outcome pathway for toxic metals inducing nephrotoxicity to aid human health risk assessment of chemical mixtures

Background: There is evidence that chronic exposure to certain metals may induce nephrotoxicity in humans, even at relatively low doses. However, there is no consensus on the biological processes involved and the human health risk assessment is complicated by frequently occurring combined exposure to metal mixtures. We aimed to address these issues by developing a common Adverse Outcome Pathway (AOP) for toxic metals and nephrotoxicity.Methods: We focused on four toxic metals, i.e. arsenic, cadmium, lead and mercury, prioritized in the context of the Horizon2020 project on Human Biomonitoring in Europe and labelled as substances of high concern by the World Health Organization. First, renal dysfunction due to proximal tubular damage was identified as their common adverse outcome using available risk assessment reports. Subsequently, the AOP was developed by collecting evidence from experimental studies.Results: We identified the following common pathway via which these metals most likely induce nephrotoxicity: thiol binding induces oxidative stress and mitochondrial dysfunction, which results in cytochrome c release followed by apoptosis in the proximal tubuli of the kidney. Evaluation of the weight-of-evidence using modified Bradford-Hill criteria, including assessment of the relevance of doses tested in experimental studies, revealed a high confidence in this AOP.Conclusions: The developed AOP enhances mechanistic understanding of metal-induced nephrotoxicity and provides scientific evidence for a causal relationship between the adverse effect and effect biomarkers. Furthermore, it can be a useful tool to facilitate and improve risk assessment of combined exposure to different metals, because it enables determination of key events that are most perturbed by the studied metals and that could be used for bioassays in the future.

Fiproles as a proxy for ecological risk assessment of mixture of fipronil and its degradates in effluent-dominated surface waters

Environmental risk assessment of complex chemical mixtures has increasingly been prioritized as a management goal, especially in the regulatory sector. Although fipronil and its three degradates (-sulfone, -sulfide and -desulfinyl) have been frequently quantified in waterways, little information is available about the likelihood and magnitude of ecological risk posed by these chemical mixtures – collectively known as fiproles – in surface water. In the present study, a probabilistic risk assessment of mixtures of fipronil and its three degradates was conducted for three effluent-dominated southern California rivers: Los Angeles River (LAR), San Gabriel River (SGR) and Santa Clara River (SCR), California, USA. The assessments, which used fiproles as an integrated proxy, were based on three levels of toxicity endpoints: median lethal concentration (LC50), half-maximal effective concentration (EC50), and lowest observed effect concentration (LOEC), to gain comprehensive assessment information. Probabilistic approaches based on species sensitivity distribution (SSD) and exposure concentration distribution (ECD) were developed with the log-logistic model by pooling the toxicity and occurrence data, respectively. The 5th percentile hazardous concentrations (HC5s) were calculated to be at low parts per billion levels, enabling these values to be used to estimate the chemical-specific benchmarks for components that lack ecotoxicity data. The single substance potentially affected fraction (ssPAF) of fiproles revealed risk levels for the three rivers in descending order: LAR ≥ SGR > SCR. The overall risk probability estimated from the joint probability curve (JPC) by Monte Carlo simulation was 1.13 ± 0.20% (LC50), 9.31 ± 1.46% (EC50), and 6.58 ± 1.43% (LOEC) for the three rivers collectively. These results derived from the fiproles indicates that fipronil and its degradates pose risks to the aquatic organisms in the surface water of the three rivers. The present study provides a methodology for the use of a proxy in the risk assessment of chemical mixtures.

Mixture Risk Drivers in Freshwater Sediments and Their Bioavailability Determined Using Passive Equilibrium Sampling.

The identification of mixture risk drivers is a great challenge for sediment assessment, especially when taking bioavailability into consideration. The bioavailable portion, which comprises the organic contaminants in pore water and the ones bound to organic carbon, was accessed by equilibrium partitioning to polydimethylsiloxane (PDMS). The exhaustive solvent and PDMS extracts were toxicologically characterized with a battery of in vitro reporter gene assays and chemically analyzed with liquid and gas chromatography coupled to high-resolution mass spectrometry. The bioavailable fractions of mixture effects and individual chemicals were mostly lower than 0.1, indicating that more than 90% of the substances are strongly bound and would not pose an immediate risk but could potentially be remobilized in the long term. Despite 655 organic chemicals analyzed, only 0.1-28% of the observed biological effects was explained by the detected compounds in whole sediments, while 0.009-3.3% was explained by bioavailable chemicals. The mixture effects were not only dominated by legacy pollutants (e.g., polycyclic aromatic hydrocarbons (PAHs) in the bioassay for activation of the aryl-hydrocarbon receptor (AhR) and oxidative stress response (AREc32)) but also by present-use chemicals (e.g., plastic additives for binding to the peroxisome proliferator-activated receptor γ (PPARγ)), with different fingerprints between whole sediments and bioavailable extracts. Our results highlight the necessity to involve different bioassays with diverse effect profiles and broader selection of contaminants along with bioavailability for the risk assessment of chemical mixtures in sediments.

An equivalency iterative algorithm for cancer risk assessment of chemical mixtures with additive effects

To better estimate cumulative cancer risks and avoid the overestimated risk from the linear extrapolation, an equivalency iterative algorithm associated with a carcinogenesis hypothesis was introduced for a mixture of chemicals with the same mode of action (MOA). A lognormal dose-response function was applied for carcinogenic chemicals. Under some circumstances, the repetitive random iterative algorithm could be transformed into the nonrepetitive one. It was also demonstrated that the equivalent value for a lognormal-based equivalency iterative algorithm with the same shape parameter was independent of the operation order. Based on the theorems of the algorithm and Plackett and Hewlett’s minimum effective dose assumption, the sum of toxicity-weighted dose for a mixture of chemicals was mathematically derived. Compared to the estimation of risk by the linear extrapolation method (e.g., cancer slope factors), the equivalency iterative algorithm for lognormal functions can avoid overestimated risk significantly, which can help better estimate the cumulative cancer risk for a mixture of chemicals with the same MOA.

The challenge of combined exposure to multiple chemicals: scientific and regulatory approaches to protect human health

Abstract Every day we are exposed to low levels of hundreds of different manmade chemicals present for example in our food, consumer products and the air we breathe. Combined exposure to multiple chemicals can lead to adverse effects on human health or the ecosystem, even if single chemicals in the mixtures are below their individual safety thresholds. However, current safety assessment practice is primarily based on understanding the potential risk posed by single chemicals rather than their 'real life' combinations. In particular, while manufactured products such as pesticide formulations or cosmetic products are covered, unintentional mixtures which are coincidentally formed such as mixtures of contaminants in indoor air, are not consistently addressed. Their composition is often unknown and changes over time. The assessment of unintentional mixtures is therefore usually limited to specific legislative sectors only, such as pesticide residues in food. Generally, methodologies to address chemical mixtures have been developed; however, there are still several data and methodological gaps to be addressed. New approach methodologies can support the filling of knowledge gaps on the toxicity and mode(s) of action of individual chemicals, to unravel links between chemical exposure and health effects. (Bio)Monitoring, modelling, and better data sharing will support the derivation of more realistic co-exposure scenarios. Considering the large number of possible combinations of chemicals in mixtures, prioritization is needed, so that actions first address mixtures of highest concern and chemicals that drive the mixture risk. As chemicals with different applications and regulated separately might lead to similar toxicological effects, it is important to consider chemical mixtures across legislative sectors. This contribution to the workshop will summarise current challenges and ways forward in the risk assessment and risk management of mixtures for protecting public health.

Synergic toxic effects of food contaminant mixtures in human cells.

Humans are exposed to multiple exogenous substances, notably through food consumption. Many of these compounds are suspected to impact human health, and their combination could exacerbate their harmful effects. We previously observed in human cells that, among the six most prevalent food contaminant complex mixtures identified in the French diet, synergistic interactions between component appeared in two mixtures compared with the response with the chemicals alone. In the present study, we demonstrated in human cells that these properties are driven only by two heavy metals in each mixture: tellurium (Te) with cadmium (Cd) and Cd with inorganic arsenic (As), respectively. It appeared that the predicted effects for these binary mixtures using the mathematical model of Chou and Talalay confirmed synergism between these heavy metals. Based on different cell biology experiments (cytotoxicity, genotoxicity, mutagenesis and DNA repair inhibition experiments), a detailed mechanistic analysis of these two mixtures suggests that concomitant induction of oxidative DNA damage and decrease of their repair capacity contribute to the synergistic toxic effect of these chemical mixtures. Overall, these results may have broad implications for the fields of environmental toxicology and chemical mixture risk assessment.

Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment

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 (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV 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 aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the 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 to chemical classes with HBM data in the context of chemical mixture 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 more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.

Application of a Framework for Grouping and Mixtures Toxicity Assessment of PFAS: A Closer Examination of Dose-Additivity Approaches.

Environmental occurrence and biomonitoring data for per- and polyfluoroalkyl substances (PFAS) demonstrate that humans are exposed to mixtures of PFAS. This article presents a new and systematic analysis of available PFAS toxicity study data using a tiered mixtures risk assessment framework consistent with United States and international mixtures guidance. The lines of evidence presented herein include a critique of whole mixture toxicity studies and analysis of dose-response models based on data from subchronic oral toxicity studies in rats. Based on available data to-date, concentration addition and relative potency factor methods are found to be inappropriate due to differences among sensitive effects and target organ potencies and noncongruent dose-response curves for the same effect endpoints from studies using the same species and protocols. Perfluorooctanoic acid and perfluorooctane sulfonic acid lack a single mode of action or molecular initiating event and our evaluation herein shows they also have noncongruent dose-response curves. Dose-response curves for long-chain perfluoroalkyl sulfonic acids (PFSAs) also significantly differ in shapes of the curves from short-chain PFSAs and perfluoroalkyl carboxylic acids evaluated, and additional differences are apparent when curves are evaluated based on internal or administered dose. Following well-established guidance, the hazard index method applied to perfluoroalkyl carboxylic acids and PFSAs grouped separately is the most appropriate approach for conducting a screening level risk assessment for nonpolymeric PFAS mixtures, given the current state-of-the science. A clear presentation of assumptions, uncertainties, and data gaps is needed before dose-additivity methods, including hazard index , are used to support risk management decisions. Adverse outcome pathway(s) and mode(s) of action information for perfluorooctanoic acid and perfluorooctane sulfonic acid and for other nonpolymer PFAS are key data gaps precluding more robust mixtures methods. These findings can guide the prioritization of future studies on single chemical and whole mixture toxicity studies.

Methodology for health risk assessment of combined exposures to multiple chemicals

Focus on risks to human health and the environment from combined exposure to multiple chemicals (“mixture risk assessment”) has increased in the last couple of decades. There has been a rise in awareness and concern in the community, especially concerning unintentional environmental exposure to unknown chemical mixtures. The Horizon 2020 project EuroMix has developed methodologies and tools for mixture risk assessment with a focus on component-based approach where substances are grouped based on toxicological considerations. Dose addition is used as the model for calculating the combined toxicity of mixture components. The methodology is anchored in the Adverse Outcome Pathway (AOP) concept, which provides a structured basis for e.g. grouping substances into assessment groups and identifying and collecting relevant toxicity data. The aim of this paper is to describes development of the methodology for mixture risk assessment and to provide detailed methodology for problem formulation, use of AOP networks for development of tiered testing strategies and grouping of substances, as well as considerations for use of dose addition methodology.

Grouping of endocrine disrupting chemicals for mixture risk assessment – Evidence from a rat study

Exposure to mixtures of endocrine disrupting chemicals may contribute to the rising incidence of hormone-related diseases in humans. Real-life mixtures are complex, comprised of chemicals with mixed modes of action, and essential knowledge is often lacking on how to group such chemicals into cumulative assessment groups, which is an essential prerequisite to conduct a chemical mixture risk assessment.We investigated if mixtures of chemicals with diverse endocrine modes of action can cause mixture effects on hormone sensitive endpoints in developing and adult rat offspring after perinatal exposure. Wistar rats were exposed during pregnancy and lactation simultaneously to either bisphenol A and butylparaben (Emix), diethylhexyl phthalate and procymidone (Amix), or a mixture of all four substances (Totalmix). In male offspring, the anogenital distance was significantly reduced and nipple retention increased in animals exposed to Amix and Totalmix, and the mixture effects were well approximated by the dose addition model. The combination of Amix and Emix responded with more marked changes on these and other endocrine-sensitive endpoints than each binary mixture on its own. Sperm counts were reduced by all exposures. These experimental outcomes suggest that the grouping of chemicals for mixture risk assessment should be based on common health outcomes rather than only similar modes or mechanisms of action. Mechanistic-based approaches such as the concept of Adverse Outcome Pathway (AOP) can provide important guidance if both the information on shared target tissues and the information on shared mode/mechanism of action are taken into account.

Distinguishing mode of action of compounds inducing craniofacial malformations in zebrafish embryos to support dose-response modeling in combined exposures

Knowledge on mode-of-action (MOA) is required to understand toxicological effects of compounds, notably in the context of risk assessment of mixtures. Such information is generally scarce, and often complicated by the existence of multiple MOAs per compound. Here, MOAs related to developmental craniofacial malformations were derived from literature, and assembled in a MOA network. A selection of gene expression markers was based on these MOAs. Next, these markers were verified by qPCR in zebrafish embryos, after exposure to reference compounds. These were: triazoles for inhibition of retinoic acid (RA) metabolism, AM580 and CD3254 for selective activation of respectively RA-receptor (RAR) and retinoid-X-receptor (RXR), dithiocarbamates for inhibition of lysyl oxidase, TCDD for activation of the aryl-hydrocarbon-receptor (AhR), VPA for inhibition of histone deacetylase (HDAC), and PFOS for activation of peroxisome proliferator-activated receptor-alpha (PPARα). Next, marker gene profiles for these reference compounds were used to map the profiles of test compounds to known MOAs. In this way, 2,4-dinitrophenol matched with the TCDD and RAR profiles, boric acid with RAR, endosulfan with PFOS, fenpropimorph with dithiocarbamates, PCB126 with AhR, and RA with triazoles and RAR profiles. Prochloraz showed no match. Activities of these compounds in ToxCast assays, and in silico analysis of binding affinity to the respective targets showed limited concordance with the marker gene expression profiles, but still confirmed the complex MOA profiles of reference and test compounds. Ultimately, this approach could be used to support modeling of mixture effects based on upfront knowledge of (dis)similarity of MOAs.

Coupling mixture reference models with DGT-perceived metal flux for deciphering the nonadditive effects of rare earth mixtures to wheat in soils

The risk assessment of mixtures of rare earth elements (REEs) is hampered by a lack of fundamental understanding of their interactions in different soil types. Here, we assessed mixture interactions and toxicity to Triticum aestivum of Y and Ce in four different soils in relation to their bioavailability. Mixture toxicity was modelled by concentration addition (CA) and independent action (IA), in combination with different expressions of exposure: three equilibrium-based doses (total soil concentrations [M]tot, free ion activity in soil solution {M3+}, and the fraction (f) of metal ions bound to the biotic ligands (BLs)) and one kinetically controlled dose ([M]flux) metrics. Upon single exposure, REE toxicity was increasingly better described when using exposure expressions based on deepened understanding of their bioavailability: [M]flux > f > {M3+} > [M]tot. The mixture analyses based on [M]tot and {M3+} displayed deviations from additivity depending on the soil type. With the parameters derived from single exposures, the BLM approach gave better predictions of mixture toxicity (R2 ~ 0.70) than when using CA and IA based on either [M]tot or {M3+} (R2 < 0.64). About 30% of the variance in toxicity remained unexplained, challenging the view that the free metal ion is the main bioavailable form under the BLM framework based on thermodynamic equilibrium. Toxicity was best described when accounting for changes in the size of the labile metal pool by using a kinetically controlled dose metric (R2 ~ 0.80). This suggests that dynamic bioavailability analysis could provide a robust basis for modeling and reconciling the interplays and toxicity of metal mixtures in different soils.

Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation

Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD50) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R2 = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.

Retrospective Risk Assessment of Chemical Mixtures in the Big Data Era: An Alternative Classification Strategy to Integrate Chemical and Toxicological Data.

Retrospective Risk Assessment of Chemical Mixtures in the Big Data Era: An Alternative Classification Strategy to Integrate Chemical and Toxicological Data.

Acting on uncertainty: real-life mixtures of endocrine disrupting chemicals

Exposure to multiple synthetic chemicals is a permanent feature of modern life. Many of these chemicals are suspected to disrupt endocrine systems of humans and animals. Endocrine disrupting chemicals (EDCs) act at very low concentrations and non-linearly, defying mainstream single-substance chemical regulation. Here we provide an analysis of findings from the first phase of the European Horizon2020-funded “EDC-MixRisk” project as a case of contemporary life-science enterprise, which addresses health-risks related to real-life exposure to mixtures of EDCs. Real-life EDC mixtures were inferred in the project from biological samples taken from pregnant women in a large epidemiological study that followed up their children over several years across major health domains; responses to these mixtures were then experimentally identified, and based on these findings, mixture risk assessment models were developed. The project consequently advocated for European chemical regulation more attentive to real-life exposure. Locating it within historical and sociological analyses of chemical exposure and within the European chemical political context, we argue that scientific uncertainty related to real-life EDC mixture exposure enables a form of epistemological approach and scientific activism, simultaneously in continuity with, and in break from, mainstream toxicology. In a chemically polluted world, this kind of science still occupies a place in the tension between public health and market-oriented regulation.

Recommendations for international harmonisation, implementation and further development of suitable scientific approaches regarding the assessment of mixture effects

Legal frameworks lay down requirements for risk assessment of combined exposure to multiple chemicals and their implementation where scientific methods are accepted by responsible authorities. In order to protect human health, an assessment of potential risks that might result from co-exposure to multiple chemical substances is requested by European legislation. Several approaches for risk assessment of mixtures of chemicals have been proposed, but none has been widely implemented in regulatory risk assessments, so far. EuroMix, an EU Horizon 2020 funded project, contributed to the improvement of internationally harmonised approaches for risk assessment of chemical mixtures. Based on in vitro and in silico tests, an integrated test strategy involving hazard and exposure assessment was developed and a web tool to conduct such assessments was provided. One further task within EuroMix was to make recommendations for international harmonisation, implementation and further development of suitable scientific approaches regarding the assessment of mixture effects. This paper briefly describes objectives and outcome of the EuroMix project as well as recent findings from OECD, WHO and EFSA addressing combined exposure to multiple chemicals. Building on this, five steps addressing further development needs and implementation of existing tools especially for risk managers and policy makers are proposed.