MASNOVO, AMATO

To address the poorly understood mixture effects of chemicals in the marine mammal dugong, we coupled equilibrium-based passive sampling in blubber to a range of in vitro bioassays for screening mixtures of bioaccumulative chemicals. The modes of action included early effect indicators along important toxicity pathways, such as induction of xenobiotic metabolism, and some integrative indicators downstream of the molecular initiating event, such as adaptive stress responses. Activation of aryl hydrocarbon receptor (AhR) and Nrf2-mediated oxidative stress response were found to be the most prominent effects, while the p53-mediated DNA damage response and NF-κB-mediated response to inflammation were not significantly affected. Although polychlorinated dibenzo-<i>p</i>-dioxins (PCDDs) quantified in the samples accounted for the majority of AhR-mediated activity, PCDDs explained less than 5% of the total oxidative stress response, despite their known ability to activate this pathway. Altered oxidative stress response was observed with both individual chemicals and blubber extracts subject to metabolic activation by rat liver S9 fraction. Metabolic activation resulted in both enhanced and reduced toxicity, suggesting the relevance and utility of incorporating metabolic enzymes into in vitro bioassays. Our approach provides a first insight into the burden of toxicologically relevant bioaccumulative chemical mixtures in dugongs and can be applied to lipid tissue of other wildlife species

Headspace-Free Setup of <i>in Vitro</i> Bioassays for the Evaluation of Volatile Disinfection By-Products

The conventional setup of <i>in vitro</i> bioassays in microplates does not prevent the loss of volatile compounds, which hampers the toxicological characterization of waterborne volatile disinfection by-products (DBPs). To minimize the loss of volatile test chemicals, we adapted four <i>in vitro</i> bioassays to a headspace-free setup using eight volatile organic compounds (four trihalomethanes, 1,1-dichloroethene, bromoethane, and two haloacetonitriles) that cover a wide range of air–water partition coefficients. The nominal effect concentrations of the test chemicals decreased by up to three orders of magnitude when the conventional setup was changed to a headspace-free setup for the bacterial cytotoxicity assay using bioluminescence inhibition of <i>Vibrio fischeri</i>. The increase of apparent sensitivity correlated significantly with the air–water partition coefficient. Purge and trap GC/MS analysis revealed a reduced loss of dosed volatile compounds in the headspace free setup (78–130% of nominal concentration) compared to a substantial loss in the conventional set up (2–13% of the nominal concentration). The experimental effect concentrations converged with the headspace-free setup to the effect concentrations predicted by a QSAR model, confirming the suitability of the headspace-free approach to minimize the loss of volatile test chemicals. The analogue headspace-free design of the bacterial bioassays for genotoxicity (umuC assay) and mutagenicity (Ames fluctuation assay) increased the number of compounds detected as genotoxic or mutagenic from one to four and zero to two, respectively. In a bioassay with a mammalian cell line applied for detecting the induction of the Nrf-2-mediated oxidative stress response (AREc32 assay), the headspace-free setup improved the apparent sensitivity by less than one order of magnitude, presumably due to the retaining effect of the serum components in the medium, which is also reflected in the reduced aqueous concentrations of compounds. This study highlights the importance of adapting bioanalytical test setups when volatile/semivolatile compounds are present in the sample to avoid the loss of chemicals and thus to avoid underestimating the toxicity of mixtures and complex environmental samples

Applicability of Passive Sampling to Bioanalytical Screening of Bioaccumulative Chemicals in Marine Wildlife

Quantification of bioaccumulative contaminants in biota is time and cost-intensive and the required extensive cleanup steps make it selective toward targeted chemical groups. Therefore tissue extracts prepared for chemical analysis are not amenable to assess the combined effects of unresolved complex mixtures. Passive equilibrium sampling with polydimethylsiloxane (PDMS) has the potential for unbiased sampling of mixtures, and the PDMS extracts can be directly dosed into cell-based bioassays. The passive sampling approach was tested by exposing PDMS to lipid-rich tissue (dugong blubber; 85% lipid) spiked with a known mixture of hydrophobic contaminants (five congeners of tetra- to octachloro-dibenzo-<i>p</i>-dioxins). The equilibrium was attained within 24 h. Lipid-PDMS partition coefficients (<i>K</i>_lip‑PDMS) ranged from 20 to 38, were independent of hydrophobicity, and within the range of those previously measured for organochlorine compounds. To test if passive sampling can be combined with bioanalysis without the need for chemical cleanup, spiked blubber-PDMS extracts were dosed into the CAFLUX bioassay, which specifically targets dioxin-like chemicals. Small quantities of lipids coextracted by the PDMS were found to affect the kinetics in the regularly applied 24-h bioassay; however, this effect was eliminated by a longer exposure period (72 h). The validated method was applied to 11 unspiked dugong blubber samples with known (native) dioxin concentrations. These results provide the first proof of concept for linking passive sampling of lipid-rich tissue with cell-based bioassays, and could be further extended to other lipid rich species and a wider range of bioanalytical end points

Environmental Risk Assessment of Fluctuating Diazinon Concentrations in an Urban and Agricultural Catchment Using Toxicokinetic–Toxicodynamic Modeling

Temporally resolved environmental risk assessment of fluctuating concentrations of micropollutants is presented. We separated the prediction of toxicity over time from the extrapolation from one to many species and from acute to sublethal effects. A toxicokinetic–toxicodynamic (TKTD) model predicted toxicity caused by fluctuating concentrations of diazinon, measured by time-resolved sampling over 108 days from three locations in a stream network, representing urban, agricultural and mixed land use. We calculated extrapolation factors to quantify variation in toxicity among species and effect types based on available toxicity data, while correcting for different test durations with the TKTD model. Sampling from the distribution of extrapolation factors and prediction of time-resolved toxicity with the TKTD model facilitated subsequent calculation of the risk of undesired toxic events. Approximately one-fifth of aquatic organisms were at risk and fluctuating concentrations were more toxic than their averages. Contribution of urban and agricultural sources of diazinon to the overall risk varied. Thus using fixed concentrations as water quality criteria appears overly simplistic because it ignores the temporal dimension of toxicity. However, the improved prediction of toxicity for fluctuating concentrations may be small compared to uncertainty due to limited diversity of toxicity data to base the extrapolation factors on

Effects of Chemicals in Reporter Gene Bioassays with Different Metabolic Activities Compared to Baseline Toxicity

High-throughput cell-based bioassays are used for chemical screening and risk assessment. Chemical transformation processes caused by abiotic degradation or metabolization can reduce the chemical concentration or, in some cases, lead to the formation of more toxic transformation products. Unaccounted loss processes may falsify the bioassay results. Capturing the formation and effects of transformation products is important for relating the in vitro effects to in vivo. Reporter gene cell lines are believed to have low metabolic activity, but inducibility of cytochrome P450 (CYP) enzymes has been reported. Baseline toxicity is the minimal toxicity a chemical can have and is caused by the incorporation of the chemical into cell membranes. In the present study, we improved an existing baseline toxicity model based on a newly defined critical membrane burden derived from freely dissolved effect concentrations, which are directly related to the membrane concentration. Experimental effect concentrations of 94 chemicals in three bioassays (AREc32, ARE-bla and GR-bla) were compared with baseline toxicity by calculating the toxic ratio (TR). CYP activities of all cell lines were determined by using fluorescence-based assays. Only ARE-bla showed a low basal CYP activity and inducibility and AREc32 showed a low inducibility. Overall cytotoxicity was similar in all three assays despite the different metabolic activities indicating that chemical metabolism is not relevant for the cytotoxicity of the tested chemicals in these assays. Up to 28 chemicals showed specific cytotoxicity with TR > 10 in the bioassays, but baseline toxicity could explain the effects of the majority of the remaining chemicals. Seven chemicals showed TR < 0.1 indicating inaccurate physicochemical properties or experimental artifacts like chemical precipitation, volatilization, degradation, or other loss processes during the in vitro bioassay. The new baseline model can be used not only to identify specific cytotoxicity mechanisms but also to identify potential problems in the experimental performance or evaluation of the bioassay and thus improve the quality of the bioassay data

Effects of Chemicals in Reporter Gene Bioassays with Different Metabolic Activities Compared to Baseline Toxicity

High-throughput cell-based bioassays are used for chemical screening and risk assessment. Chemical transformation processes caused by abiotic degradation or metabolization can reduce the chemical concentration or, in some cases, lead to the formation of more toxic transformation products. Unaccounted loss processes may falsify the bioassay results. Capturing the formation and effects of transformation products is important for relating the in vitro effects to in vivo. Reporter gene cell lines are believed to have low metabolic activity, but inducibility of cytochrome P450 (CYP) enzymes has been reported. Baseline toxicity is the minimal toxicity a chemical can have and is caused by the incorporation of the chemical into cell membranes. In the present study, we improved an existing baseline toxicity model based on a newly defined critical membrane burden derived from freely dissolved effect concentrations, which are directly related to the membrane concentration. Experimental effect concentrations of 94 chemicals in three bioassays (AREc32, ARE-bla and GR-bla) were compared with baseline toxicity by calculating the toxic ratio (TR). CYP activities of all cell lines were determined by using fluorescence-based assays. Only ARE-bla showed a low basal CYP activity and inducibility and AREc32 showed a low inducibility. Overall cytotoxicity was similar in all three assays despite the different metabolic activities indicating that chemical metabolism is not relevant for the cytotoxicity of the tested chemicals in these assays. Up to 28 chemicals showed specific cytotoxicity with TR > 10 in the bioassays, but baseline toxicity could explain the effects of the majority of the remaining chemicals. Seven chemicals showed TR < 0.1 indicating inaccurate physicochemical properties or experimental artifacts like chemical precipitation, volatilization, degradation, or other loss processes during the in vitro bioassay. The new baseline model can be used not only to identify specific cytotoxicity mechanisms but also to identify potential problems in the experimental performance or evaluation of the bioassay and thus improve the quality of the bioassay data

Mixture Toxicity Revisited from a Toxicogenomic Perspective

The advent of new genomic techniques has raised expectations that central questions of mixture toxicology such as for mechanisms of low dose interactions can now be answered. This review provides an overview on experimental studies from the past decade that address diagnostic and/or mechanistic questions regarding the combined effects of chemical mixtures using toxicogenomic techniques. From 2002 to 2011, 41 studies were published with a focus on mixture toxicity assessment. Primarily multiplexed quantification of gene transcripts was performed, though metabolomic and proteomic analysis of joint exposures have also been undertaken. It is now standard to explicitly state criteria for selecting concentrations and provide insight into data transformation and statistical treatment with respect to minimizing sources of undue variability. Bioinformatic analysis of toxicogenomic data, by contrast, is still a field with diverse and rapidly evolving tools. The reported combined effect assessments are discussed in the light of established toxicological dose–response and mixture toxicity models. Receptor-based assays seem to be the most advanced toward establishing quantitative relationships between exposure and biological responses. Often transcriptomic responses are discussed based on the presence or absence of signals, where the interpretation may remain ambiguous due to methodological problems. The majority of mixture studies design their studies to compare the recorded mixture outcome against responses for individual components only. This stands in stark contrast to our existing understanding of joint biological activity at the levels of chemical target interactions and apical combined effects. By joining established mixture effect models with toxicokinetic and -dynamic thinking, we suggest a conceptual framework that may help to overcome the current limitation of providing mainly anecdotal evidence on mixture effects. To achieve this we suggest (i) to design studies to establish quantitative relationships between dose and time dependency of responses and (ii) to adopt mixture toxicity models. Moreover, (iii) utilization of novel bioinformatic tools and (iv) stress response concepts could be productive to translate multiple responses into hypotheses on the relationships between general stress and specific toxicity reactions of organisms

DataSheet1_Experimental exposure assessment of designed chemical mixtures in cell-based in vitro bioassays.PDF

Cell-based bioassays are useful tools for the effect assessment of complex mixtures, but so far exposure assessment has not been performed for mixtures of chemicals. In the present study, cytotoxicity and activation of oxidative stress response were measured for three designed chemical mixtures with up to twelve components. The measurements of biological responses were complemented by concentration measurements using solid-phase microextraction to derive the freely dissolved concentrations of the mixtures (Cfree,mix). The tested mixtures showed slightly higher cytotoxic effects than predicted by the concentration addition model. Nominal and freely dissolved effect concentrations of the mixtures were very similar (within a factor of 1.5), but nominal concentrations (Cnom) and Cfree of the individual mixture components were only similar for the hydrophilic chemicals (e.g., caffeine, coumarin, lamotrigine). For hydrophobic (e.g., fluoranthene) and acidic chemicals (e.g., diclofenac, naproxen) Cfree was up to 648 times lower than Cnom. Chemicals were dosed in equipotent nominal concentration ratios and therefore contributed equally to the detected effects. Hydrophilic chemicals with low potency dominated Cnom,mix (up to 95%) and Cfree,mix (up to 99%). Several mixture components (e.g., diclofenac, ibuprofen, naproxen and warfarin) showed increasing free fractions with increasing Cnom,mix and therefore also a concentration-dependent contribution to Cfree,mix. Based on the findings of this study, we concluded that Cnom,mix will be sufficient for evaluating the toxicity of mixtures that contain chemicals with diverse physicochemical properties at low concentration levels. In contrast, for risk assessment purposes and quantitative in vitro to in vivo extrapolations, Cfree,mix is a better parameter because the in vitro responses can be related to freely dissolved concentrations in human plasma.</p

Significance of Xenobiotic Metabolism for Bioaccumulation Kinetics of Organic Chemicals in <i>Gammarus pulex</i>

Bioaccumulation and biotransformation are key toxicokinetic processes that modify toxicity of chemicals and sensitivity of organisms. Bioaccumulation kinetics vary greatly among organisms and chemicals; thus, we investigated the influence of biotransformation kinetics on bioaccumulation in a model aquatic invertebrate using fifteen ¹⁴C-labeled organic xenobiotics from diverse chemical classes and physicochemical properties (1,2,3-trichlorobenzene, imidacloprid, 4,6-dinitro-o-cresol, ethylacrylate, malathion, chlorpyrifos, aldicarb, carbofuran, carbaryl, 2,4-dichlorophenol, 2,4,5-trichlorophenol, pentachlorophenol, 4-nitrobenzyl-chloride, 2,4-dichloroaniline, and sea-nine (4,5-dichloro-2-octyl-3-isothiazolone)). We detected and identified metabolites using HPLC with UV and radio-detection as well as high resolution mass spectrometry (LTQ-Orbitrap). Kinetics of uptake, biotransformation, and elimination of parent compounds and metabolites were modeled with a first-order one-compartment model. Bioaccumulation factors were calculated for parent compounds and metabolite enrichment factors for metabolites. Out of 19 detected metabolites, we identified seven by standards or accurate mass measurements and two via pathway analysis and analogies to other compounds. 1,2,3-Trichlorobenzene, imidacloprid, and 4,6-dinitro-o-cresol were not biotransformed. Dietary uptake contributed little to overall uptake. Differentiation between parent and metabolites increased accuracy of bioaccumulation parameters compared to total ¹⁴C measurements. Biotransformation dominated toxicokinetics and strongly affected internal concentrations of parent compounds and metabolites. Many metabolites reached higher internal concentrations than their parents, characterized by large metabolite enrichment factors

Most Oxidative Stress Response In Water Samples Comes From Unknown Chemicals: The Need For Effect-Based Water Quality Trigger Values

The induction of adaptive stress response pathways is an early and sensitive indicator of the presence of chemical and non-chemical stressors in cells. An important stress response is the Nrf-2 mediated oxidative stress response pathway where electrophilic chemicals or chemicals that cause the formation of reactive oxygen species initiate the production of antioxidants and metabolic detoxification enzymes. The AREc32 cell line is sensitive to chemicals inducing oxidative stress and has been previously applied for water quality monitoring of organic micropollutants and disinfection byproducts. Here we propose an algorithm for the derivation of effect-based water quality trigger values for this end point that is based on the combined effects of mixtures of regulated chemicals. Mixture experiments agreed with predictions by the mixture toxicity concept of concentration addition. The responses in the AREc32 and the concentrations of 269 individual chemicals were quantified in nine environmental samples, ranging from treated effluent, recycled water, stormwater to drinking water. The effects of the detected chemicals could explain less than 0.1% of the observed induction of the oxidative stress response in the sample, affirming the need to use effect-based trigger values that account for all chemicals present