Umeå University's logo

Humans and wildlife are exposed to a complex mixture of anthropogenic chemicals of which only a few have been subjected to regulations. Chemical risk assessment is currently based on evaluating single chemicals, which is costly, time-consuming, and neglect toxicokinetic and toxicodynamic mixture effects. This study focused on interaction effects on the absorption, distribution, metabolism and excretion (ADME) processes of selected chemicals representing potential modulators of these processes. Adult female zebrafish (Danio rerio) were exposed to selected mixture of 11 chemicals and bioconcentration factors (BCFs) on tissue level were determined for 9 of them: bisphenol A (BPA), bisphenol AF (BPAF), bisphenol Z (BPZ), triclosan, tribromophenol, pentachlorophenol, heptafluorobutyric acid (PFBA), perfluorobutanesulfonic acid (PFBS), and perfluorooctanesulfonic acid (PFOS). Comparison of BCFs of bisphenols obtained from single chemical exposure experiments versus the current study revealed no statistically significant differences (p > 0.05), implying no mixture effects on kinetics of bisphenols at investigated concentrations. The same conclusion was reached using two physiologically based kinetic (PBK) models, developed for individual bisphenols and per- and polyfluoroalkyl substances (PFAS), showing good model fit for BPA, BPZ, BPAF, and PFOS. To simulate exposure scenarios where kinetic interaction effects may occur through competitive protein binding in blood, a new PBK model was developed. Simulations where zebrafish were dosed with BPA and BPZ, individually, and combined with varying levels of PFOS, showed that competitive binding to serum proteins alter tissue levels of bisphenols when levels of PFOS exceeded 1 μg/L. This indicates that chemicals acting in concert could perturb ADME but only at higher levels or in complex mixtures.

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Materials in car cabins contain performance-enhancing semi-volatile organic compounds (SVOCs). As these SVOCs are not chemically bound to the materials, they can emit from the materials at slow rates to the surrounding, causing human exposure. This study aimed at increasing the understanding on abundance of SVOCs in car cabins by studying 18 potential endocrine disrupting chemicals in car cabin air (gas phase and airborne particles) and dust. We also studied how levels of these chemicals varied by temperature inside the car cabin along with ventilation settings, relevant to human exposure. A positive correlation was observed between temperature and SVOC concentration in both the gas and the particle phase, where average gas phase levels at 80 °C were a factor of 18–16,000 higher than average levels at 25 °C, while average particle phase levels were a factor of 4.6–40,000 higher for the studied substances. This study also showed that levels were below the limit of detection for several SVOCs during realistic driving conditions, i.e., with the ventilation activated. To limit human exposure to SVOCs in car cabins, it is recommended to ventilate a warm car before entering and have the ventilation on during driving, as both temperature and ventilation have a significant impact on SVOC levels.

BACKGROUND: Polychlorinated alkanes (PCAs) constitute a large group of individual congeners originating from commercial chlorinated paraffin (CP) products with carbon chain lengths of PCAs-C_10-13, PCAs-C_14-17, and PCAs-C_18-32, occasionally containing PCAs-C_6-9 impurities. The extensive use of CPs has led to global environmental pollution of PCAs. This study aimed to quantify PCAs in paired serum and breast milk of lactating Swedish mothers, exploring their concentration relationship.

METHODS: Twenty-five paired samples of mothers' blood serum and breast milk were analysed and concentrations were determined for PCAs C_6-32 and compared to 4,4'-DDE, the PCB congener 2,2',4,4',5,5'-hexachlorobiphenyl (CB-153), and hexachlorobenzene (HCB).

RESULTS: The median concentrations of PCAs-C_6-9, PCAs-C_10-13, PCAs-C_14-17, PCAs-C_18-32 and ΣPCAs in serum were 14, 790, 520, 16 and 1350 ng/g lipid weight (lw), respectively, and in breast milk 0.84, 36, 63, 6.0 and 107 ng/g lw. Levels of 4,4'-DDE, CB-153 and HCB were comparable in the two matrices, serum and breast milk at 17, 12 and 4.9 ng/g lw. The results show significant differences of PCAs-C_10-13 and PCAs-C_14-17 in breast milk with 22- and 6.2-times lower lw-based concentrations than those measured in serum. On wet weight the differences serum/breast milk ratios of PCAs-C_6-9, PCAs-C_10-13, PCAs-C_14-17, PCAs-C_18-32 and ΣPCAs were 1.7, 3.2, 1.0, 0.4 and 1.6, respectively, while the ratio for 4,4'-DDE, CB-153 and HCB were each close to 0.1.

CONCLUSION: Swedish lactating mothers had high serum concentrations of PCAs-C_10-13 and PCAs-C_14-17, with the ΣPCAs median serum concentration of 1350 ng/g lw. The breast milk concentration, although considerably lower at 107 ng/g lw, still surpassed those of 4,4'-DDE, CB-153 and HCB, suggesting an exposure risk of infants to PCAs. The variation in blood and breast milk accumulation between PCAs and studied legacy POPs, is rarely discussed but warrants further studies on partitioning properties as well as associated toxicological implications.

Per- and polyfluoroalkyl substances (PFAS) are ubiquitously distributed in the aquatic environment. They include persistent, mobile, bioaccumulative, and toxic chemicals and it is therefore critical to increase our understanding on their adsorption, distribution, metabolism, excretion (ADME). The current study focused on uptake of seven emerging PFAS in zebrafish (Danio rerio) and their potential maternal transfer. In addition, we aimed at increasing our understanding on mixture effects on ADME by developing a physiologically based kinetic (PBK) model capable of handling co-exposure scenarios of any number of chemicals. All studied chemicals were taken up in the fish to varying degrees, whereas only perfluorononanoate (PFNA) and perfluorooctanoate (PFOA) were quantified in all analysed tissues. Perfluorooctane sulfonamide (FOSA) was measured at concerningly high concentrations in the brain (C_max over 15 μg/g) but also in the liver and ovaries. All studied PFAS were maternally transferred to the eggs, with FOSA and 6:2 perfluorooctane sulfonate (6,2 FTSA) showing significant (p < 0.02) signs of elimination from the embryos during the first 6 days of development, while perfluorobutane sulfonate (PFBS), PFNA, and perfluorohexane sulfonate (PFHxS) were not eliminated in embryos during this time-frame. The mixture PBK model resulted in >85 % of predictions within a 10-fold error and 60 % of predictions within a 3-fold error. At studied levels of PFAS exposure, competitive binding was not a critical factor for PFAS kinetics. Gill surface pH influenced uptake for some carboxylates but not the sulfonates. The developed PBK model provides an important tool in understanding kinetics under complex mixture scenarios and this use of New Approach Methodologies (NAMs) is critical in future risk assessment of chemicals and early warning systems.

Identification of Endocrine-Disrupting Chemicals (EDCs) in a regulatory context requires a high level of evidence. However, lines of evidence (e.g. human, in vivo, in vitro or in silico) are heterogeneous and incomplete for quantifying evidence of the adverse effects and mechanisms involved. To date, for the regulatory appraisal of metabolism-disrupting chemicals (MDCs), no harmonised guidance to assess the weight of evidence has been developed at the EU or international level. To explore how to develop this, we applied a formal Expert Knowledge Elicitation (EKE) approach within the European GOLIATH project. EKE captures expert judgment in a quantitative manner and provides an estimate of uncertainty of the final opinion. As a proof of principle, we selected one suspected MDC -triphenyl phosphate (TPP) - based on its related adverse endpoints (obesity/adipogenicity) relevant to metabolic disruption and a putative Molecular Initiating Event (MIE): activation of peroxisome proliferator activated receptor gamma (PPARγ). We conducted a systematic literature review and assessed the quality of the lines of evidence with two independent groups of experts within GOLIATH, with the objective of categorising the metabolic disruption properties of TPP, by applying an EKE approach. Having followed the entire process separately, both groups arrived at the same conclusion, designating TPP as a “suspected MDC” with an overall quantitative agreement exceeding 85%, indicating robust reproducibility. The EKE method provides to be an important way to bring together scientists with diverse expertise and is recommended for future work in this area.

Prenatal exposure to a mixture (MIX N) of eight endocrine-disrupting chemicals has been associated with language delay in children in a Swedish pregnancy cohort. A novel approach was proposed linking this epidemiological association with experimental evidence, where the effect of MIX N on thyroid hormone signaling was assessed using the Xenopus eleuthero-embryonic thyroid assay (XETA OECD TG248). From this experimental data, a point of departure (PoD) was derived based on OECD guidance. Our aim in the current study was to use updated toxicokinetic models to compare exposures of women of reproductive age in the US population to MIX N using a Similar Mixture Approach (SMACH). Based on our findings, 66% of women of reproductive age in the US (roughly 38 million women) had exposures sufficiently similar to MIX N. For this subset, a Similar Mixture Risk Index (SMRI_HI) was calculated comparing their exposures to the PoD. Women with SMRI_HI > 1 represent 1.1 million women of reproductive age. Older women, Mexican American and other/multi race women were less likely to have high SMRI_HI values compared to Non-Hispanic White women. These findings indicate that a reference mixture of chemicals identified in a Swedish cohort—and tested in an experimental model for establishment of (PoDs)—is also of health relevance in a US population.

Zebrafish embryos (ZFE) is a widely used model organism, employed in various research fields including toxicology to assess e.g., developmental toxicity and endocrine disruption. Variation in effects between chemicals are difficult to compare using nominal dose as toxicokinetic properties may vary. Toxicokinetic (TK) modeling is a means to estimate internal exposure concentration or dose at target and to enable extrapolation between experimental conditions and species, thereby improving hazard assessment of potential pollutants. In this study we advance currently existing TK models for ZFE with physiological ZFE parameters and novel experimental bisphenol data, a class of chemicals with suspected endocrine activity. We developed a five-compartment model consisting of water, plastic, chorion, yolk sack and embryo in which surface area and volume changes as well as the processes of biotransformation and blood circulation influence mass fluxes. For model training and validation, we measured internal concentrations in ZFE exposed individually to BPA, bisphenol AF (BPAF) and Z (BPZ). Bayesian inference was applied for parameter calibration based on the training data set of BPZ. The calibrated TK model predicted internal ZFE concentrations of the majority of external test data within a 5-fold error and half of the data within a 2-fold error for bisphenols A, AF, F, and tetrabromo bisphenol A (TBBPA). We used the developed model to rank the hazard of seven bisphenols based on predicted internal concentrations and measured in vitro estrogenicity. This ranking indicated a higher hazard for BPAF, BPZ, bisphenol B and C (BPB, BPC) than for BPA.

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.