Application of biotic ligand and toxicokinetic-toxicodynamic modeling to predict the accumulation and toxicity of metal mixtures to zebrafish larvae

Application of toxicokinetic-toxicodynamic models in the aquatic ecological risk assessment of metals: A review

The issue of toxic metal pollution is a considerable environmental concern owing to its complex nature, spatial and temporal variability, and susceptibility to environmental factors. Current water quality criteria and ecological risk assessments of metals are based on single-metal toxicity data from short-term, simplified indoor exposure conditions, ignoring the complexity of actual environmental conditions. This results in increased uncertainty in predicting toxic metal toxicity and risk assessment. Using appropriate bioavailability and effect modeling of metals is critical for establishing environmental quality standards and performing risk assessments for metals. Traditional dose-effect models are based on a static statistical relationship and fall short of revealing the bioavailability and effect processes of metals and do not effectively assess ecological impacts under complex exposure conditions. This paper summarizes the toxicokinetic-toxicodynamic (TK-TD) model, which is gaining interest in environmental and ecotoxicological research. The key concepts, and theories of its construction theories, are discussed and the application of the TK-TD model in toxicity prediction and risk assessment of different metals in the aquatic environment, and trends in the development of the TK-TD model are highlighted. The findings of our review prove that the TK-TD model can effectively predict toxic metal toxicity in real time and under complex exposure conditions in the future.

Assessing viral freshwater hazard using a toxicokinetic model and Dreissena polymorpha

The detection all pathogenic enteric viruses in water is expensive, time-consuming, and limited by numerous technical difficulties. Consequently, using reliable indicators such as F-specific RNA phages (FRNAPH) can be well adapted to assess the risk of viral contamination of fecal origin in surface waters. However, the variability of results inherent to the water matrix makes it difficult to use them routinely and to interpret viral risk. Spatial and temporal variability of surface waters can lead to underestimate this risk, in particular in the case of low loading. The use of bivalve mollusks as accumulating systems appears as a promising alternative, as recently highlighted with the freshwater mussel Dreissena polymorpha, but its capacity to accumulate and depurate FRNAPH needs to be better understood and described. The purpose of this study is to characterise the kinetics of accumulation and elimination of infectious FRNAPH by D. polymorpha in laboratory conditions, formalised by a toxico-kinetic (TK) mechanistic model. Accumulation and depuration experiments were performed at a laboratory scale to determine the relationship between the concentration of infectious FRNAPH in water and the concentration accumulated by D. polymorpha. The mussels accumulated infectious FRNAPH (3-5.4 × 104 PFU/g) in a fast and concentration-dependent way in only 48 h, as already recently demonstrated. The second exposure demonstrated that the kinetics of infectious FRNAPH depuration by D. polymorpha was independent to the exposure dose, with a T90 (time required to depurate 90 % of the accumulated concentration) of approximately 6 days. These results highlight the capacities of D. polymorpha to detect and reflect the viral pollution in an integrative way and over time, which is not possible with point water sampling. Different TK models were fitted based on the concentrations measured in the digestive tissues (DT) of D. polymorpha. The model has been developed to formalise the kinetics of phage accumulation in mussels tissues through the simultaneous estimation of accumulation and depuration rates. This model showed that accumulation depended on the exposure concentration, while depuration did not. Standardized D. polymorpha could be easily transplanted to the environment to predict viral concentrations using the TK model defined in the present study to predict the level of contamination of bodies of water on the basis of the level of phages accumulated by the organisms. It will be also provide a better understanding of the dynamics of the virus in continental waters at different time and spatial scales, and thereby contribute to the protection of freshwater resources.

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.

Investigation and prediction of the biotoxicity of Cu2+ to Chlorella vulgaris: modification of the biotic ligand model

The increased copper ion (Cu2+) concentrations in aquatic ecosystem significantly influence the environmental quality and ecosystem safety, while information on the Cu2+ biotoxicity to aquatic microorganisms and the models for biotoxicity prediction are still unclear. In this study, the toxicities of Cu2+ to Chlorella vulgaris under different environmental conditions (e.g., Na+, K+, Ca2+, Mg2+, pH, and dissolved organic matter) were explored, with the experimental results in comparison with those predicted by the biotic ligand model (BLM). Results showed that increased Cu2+ concentration caused obvious toxicities to C. vulgaris, whereas the commonly occurring cations and dissolved organic matters can protect the metabolism system of C. vulgaris. The presence of extracellular polymeric substances (EPS) matrix can alleviate the biotoxicity via increasing the surface biosorption but decreasing cell internalization of Cu2+ in C. vulgaris. Due to the presence of EPS matrix, the experimental biotoxicity results under each condition were significantly lower than those predicted by the BLM model, which was thus modified via taking the EPS matrix as the supplement of allochthonous organic matters. After that, the modified BLM was characterized with a higher degree of precision and can be used in natural waters for biotoxicity prediction. Results obtained can enhance our insights into the ecological effects and biotoxicity prediction of heavy metals in natural aquatic ecosystems.

The impaired swim bladder via ROS-mediated inhibition of the Wnt / Hedgehog pathway in zebrafish embryos exposed to eight toxic chemicals and binary chemical mixtures

To comprehensively explore the potential toxicity of aquatic organisms exposed to chlorinated or brominated flame retardants (BFRs) and metals mixtures, it is necessary to find a common pathway to relate local toxic targeted sites or organs. A key challenge in environmental risk assessment (ERA) is how to clarify the same or different sites or organs of toxic action in a species after exposure to individual chemicals or chemical mixtures. In this study, zebrafish embryo was used to evaluate the sub-lethal toxicity (swim bladder damage) of tris(2,3-dibromo propyl) isocyanurate (TBC), chlorinated paraffins (CPs), hexabromocyclododecane (HBCD), Cu, Cd, Pb, Ag, and Zn through optical microscopy methods, and corresponding sub-lethal molecular levels (inflammation-related enzymes [deiodinase (DIO) enzymes] and transcriptional levels of key genes) in fish through quantitative real-time PCR (qRT-PCR). The tested chemicals all caused failed inflation of the swim bladder, as indicated by activity inhibition of type 2 iodothyronine deiodinase enzyme. Following embryonic exposure to respective TBC + Cu, HBCD + TBC, and Cd + Pb mixtures, as the concentration of the respective Cu, TBC, and Pb increased, the deformity of the swim bladder increased, as also indicated by activity inhibition of type 2 iodothyronine deiodinase enzyme. Additionally, eight chemicals down-regulated Wnt (wnt3, wnt9b, fzd3b, wnt1, fzd5, and fdz1) signaling pathways, which were neurotoxic responses to individual chemical treatments and Hedgehog (ihh, shh, ptc1 and ptc2) signaling pathways. Moreover, excessive ROS induced by eight chemicals effectively induced defects in the swim bladder and Wnt/Hedgehog signaling, which also be proved in respective TBC + Cu, HBCD + TBC, and Cd + Pb mixture treatments. Our results first revealed that eight chemicals caused swim bladder developmental defects via ROS-mediated inhibition of the Wnt and Hedgehog pathways, which revealed the common targeted sites or organs (swim bladders) for further studying the toxic mechanisms underlying the chemical mixtures.

A refined toxicokinetic model for quantifying the interaction between Cd and Cu in zebrafish larvae

The interaction between metals is ubiquitous, but there is still a lack of quantitative models considering the interaction between metals, which leads to the deviations in predicting the joint toxicity of metals. The present study estimated the uptake rate constants (kin) and elimination rate constants (kout) and elucidated how the presence of one metal (Cu or Cd) affects the absorption and excretion of another metal (Cd or Cu) in zebrafish larvae. The results showed that Cd and Cu inhibited each other in the process of absorption and excretion by comparing separately kin and kout of Cd or Cu with the other metal Cu or Cd mixed concentrations increased, thereby affecting the Cd and Cu bioaccumulation in the zebrafish larvae. Then the interactions between Cd and Cu in the uptake and elimination processes were quantified to obtain a refined toxicokinetic model. Verification with independent experiment data showed that the refined toxicokinetic model could significantly improve the prediction of the Cd or Cu bioaccumulation in the zebrafish larvae. This study contributes to understand the toxicokinetic process of the Cd-Cu mixture in the zebrafish larvae, and the developed model could be used to predict the toxicity of the metal mixtures.

Effect of dissolved organic matter on the bioavailability and toxicity of cadmium in zebrafish larvae: Determination based on toxicokinetic-toxicodynamic processes

The presence of dissolved organic matter (DOM) strongly influences the bioavailability of metals in aquatic environments; however, the association between the binding activities and the concentrations of DOM compositions is not well documented, leading to uncertainties in metal toxicity assessment. We creatively quantify the mitigation and acceleration effects of DOM compositions on cadmium (Cd) bioaccumulation and toxicity in zebrafish larvae using abiotic ligand (ABLs) and biotic ligand (BLs) in a toxicokinetic-toxicodynamic (TK-TD) model. The BL-TK-TD model could accurately predict the protective effect of fulvic acid while overestimating the complexing capacity of citric acid. The model also could successfully simulate the protective effects of native DOM in most cases from 32 natural water bodies in China. The observed LC50 values of Cd showed a peak effect for the native DOM fraction comprising hydrophilic acidic contents (3.55 ± 0.44 mg L ^- 1) in natural water from 32 sites. The BL-TK-TD model provides practically useful information to identify the effect of different DOM compositions on metal bioavailability and toxicity in aquatic environments and guides future water management policies aimed at controlling aquatic heavy metal pollution.

Physiologically based kinetic modelling based prediction of in vivo rat and human acetylcholinesterase (AChE) inhibition upon exposure to diazinon

The present study predicts in vivo human and rat red blood cell (RBC) acetylcholinesterase (AChE) inhibition upon diazinon (DZN) exposure using physiological based kinetic (PBK) modelling-facilitated reverse dosimetry. Due to the fact that both DZN and its oxon metabolite diazoxon (DZO) can inhibit AChE, a toxic equivalency factor (TEF) was included in the PBK model to combine the effect of DZN and DZO when predicting in vivo AChE inhibition. The PBK models were defined based on kinetic constants derived from in vitro incubations with liver fractions or plasma of rat and human, and were used to translate in vitro concentration-response curves for AChE inhibition obtained in the current study to predicted in vivo dose-response curves. The predicted dose-response curves for rat matched available in vivo data on AChE inhibition, and the benchmark dose lower confidence limits for 10% inhibition (BMDL10 values) were in line with the reported BMDL10 values. Humans were predicted to be 6-fold more sensitive than rats in terms of AChE inhibition, mainly because of inter-species differences in toxicokinetics. It is concluded that the TEF-coded DZN PBK model combined with quantitative in vitro to in vivo extrapolation (QIVIVE) provides an adequate approach to predict RBC AChE inhibition upon acute oral DZN exposure, and can provide an alternative testing strategy for derivation of a point of departure (POD) in risk assessment.

Understanding the effects of metal pre-exposure on the sensitivity of zebrafish larvae to metal toxicity: A toxicokinetics-toxicodynamics approach

Organisms are increasingly tolerant to metal toxicity in the natural ecosystems, which did not match the results of the environmental risk assessment (ERA) of metals based on toxicity data from organisms in the laboratory. Studies have described the effects of pre-exposure to metals on metal toxicity tolerance in terms of the toxicokinetic (TK) process; however, the toxicodynamic (TD) process may be more susceptible to metal pre-exposure. Therefore, to determine whether pre-exposure to low concentrations of silver (Ag) or cadmium (Cd) affects the metal TK and TD processes of zebrafish (Danio rerio) larvae, we investigated four TK-TD model parameters that control tolerance and sensitivity to metal toxicity on the survival. Our results showed that the killing rate (k_s) of larvae exposed to high Cd concentrations was significantly lower following pre-exposure to 10 μg/L Cd than that of larvaenot pre-exposed. However, the k_s for high Ag concentrations was significantly higher in zebrafish larvae following pre-exposure to 2 μg/L Ag than in larvae not pre-exposed. In other words, a one-day pre-exposure to 2 µg/L Ag rendered the larvae more sensitive to Ag during a subsequent 4-day exposure to higher Ag concentrations, whereas a one-day pre-exposure to 10 µg/L Cd rendered the larvae more tolerance to Cd during a subsequent 4-day exposure to higher Cd concentrations. Our results further the current understanding of toxic metal tolerance mechanisms, both in TK and TD processes, and they will guide future laboratory studies to assess actual pre-exposure scenarios that occur in natural environments. Thus, our study can help reduce uncertainty in testing and improve ecological management concerning metal risk assessments.

Quantifying the effect of nano-TiO2 on the toxicity of lead on C. dubia using a two-compartment modeling approach

Nanoparticles (NPs) can significantly influence toxicity imposed by toxic metals. However, this impact has not been quantified. In this research, we investigated the effect of nano-TiO₂ on lead (Pb) accumulation and the resultant toxicity using water flea Ceriodaphnia dubia (C. dubia) as the testing organism. We used a two-compartment modeling approach, which included a two-compartment accumulation model and a toxicodynamic model, on the basis of Pb body tissue accumulation, to quantify the impact of nano-TiO₂ on Pb toxicity. The effect of algae on the combined toxicity of Pb and nano-TiO₂ was also quantified. The two-compartment accumulation model could well quantify Pb accumulation kinetics in two-compartments of C. dubia, the gut and the rest of the body tissue in the presence of nano-TiO₂. Modeling results suggested that the gut quickly accumulates Pb through active uptake from the mouth, but the rest of the body tissue slowly accumulates Pb from the gut. The predicted Pb distribution within C. dubia was verified by depuration modeling results from an independent depuration test. The survivorship of C. dubia as a function of Pb accumulated in the body tissue and exposure time can be well described using a toxicodynamic model. The effects of algae on Pb accumulation in different compartments of C. dubia and the toxicity in the presence of nano-TiO₂ were also well described using the two-compartment modeling approach. Therefore, the novel two-compartment modeling approach provides a useful tool for assessing the effect of NPs on aquatic ecosystems where toxic metals are present.

Predicting copper toxicity in zebrafish larvae under complex water chemistry conditions by using a toxicokinetic-toxicodynamic model

Multiple water chemistry parameters influence metal toxicity in natural waters and accurate quantification of those influences may accelerate the development of site-specific water quality criteria (WQC) and further execute metal risk assessment for better protection of aquatic biota. Here, we investigated the effects of water chemistry parameters on copper (Cu) toxicity of larval zebrafish (Danio rerio) and then incorporated the effects of key parameters in a Toxicokinetic and Toxicodynamic (TK-TD) model. Further, the proposed TK-TD model was used to predict Cu toxicity in laboratory artificial waters as well as natural water samples. The predictive performance of the TK-TD model was evaluated in comparison to the biotic ligand model (BLM). The results showed that increasing Ca, Mg, pH, and fulvic acid (FA) levels significantly mitigated Cu toxicity in larvae, while K and Na levels had no significant effect on Cu toxicity. A predictive TK-TD model based on these data described 91 % of Cu accumulation and 87 % of survival of larvae exposed to Cu under 0, 2.5, 5, 10 mg/L FA. Compared with BLM, TK-TD model predicted better Cu accumulation and toxicity for an independent dataset in low DOC concentration (<10.95 mg L^-1) of 9 sites in Haihe river (Tianjin, China) media during 96 h exposure. The BLM under-predicted the acute Cu toxicity to larvae when compared with observed values. In high DOC concentration (13.12-17.78 mg L^-1) among three field sites, BLM and TK-TD model both under-predicted the acute Cu toxicity to larvae when compared with observed values. Our research provides a TK-TD approach for predicting Cu toxicity under complex water chemistry conditions and deriving Cu-WQC in different scenarios where there exist limits for using the BLM.

Different factors determined the toxicokinetics of organic chemicals and nanomaterials exposure to zebrafish (Danio Rerio)

Little is known about how the chemical properties (molecular structure, such as the hydrophobic and hydrophilic end group for organic chemical, and particle size for nanomaterials (NMs)) quantitatively affect the toxicokinetics (TK) in organisms especially in short-term, single-species studies. A novel method based on a first-order one compartment TK model which described the monophasic uptake pattern and two-compartment TK model which adequately described the biphasic metabolism pattern was used to determine the bioconcentration and TK rate constants of organic compounds (n = 17) and nanomaterials (NMs, n = 7) in zebrafish. For both one and two compartment model, the uptake (k_in) and elimination (k_out) rate constants were fitted using a one- and two-compartment first-order kinetic model, and bioconcentration factors (BCF) and 95% depuration times (t₉₅) for all tested chemicals were calculated, respectively. The results showed that there was significant difference in TK parameters k_in, k_out, and BCF between organic chemicals and nano metal oxides. For organic compounds, significant correlations were found between the k_in and BCF and the octanol-water partition coefficient (K_ow) and molecular mass. For nano metal oxides, there was a significant negative correlation between the k_in or BCF and particle size, but a positive correlation between k_in and Zeta potential of nanoparticles and also a significant positive correlation between k_out and particle size or specific surface area. Those findings indicated that NMs particle size does matter in biological influx and efflux processes. Our results suggest that the TK process for organic compound and NMs are correlated by different chemical properties and highlight that the K_ow, the absorption k_in, metabolism k₁₂ and k₂₁, elimination rate k_out, and all the parameters that enable the prediction and partitioning of chemicals need to be precisely determined in order to allow an effective TK modeling. It would therefore appear that the TK process of untested chemicals by a fish may be extrapolated from known chemical properties.

Comparison of the General Threshold Model of Survival and Dose-Response Models in Simulating the Acute Toxicity of Metals to Danio rerio

We exposed zebrafish (Danio rerio) to different concentrations of lead and cadmium, and monitored them for survival at 24, 48, 72, and 96 h. Metal toxicity was predicted and compared using the dose-response and general threshold survival models in terms of required data sets, fit performance, and applicability. Environ Toxicol Chem 2019;38:2169-2177. © 2019 SETAC.

Applications of dynamic models in predicting the bioaccumulation, transport and toxicity of trace metals in aquatic organisms

This review evaluates the three dynamic models (biokinetic model: BK, physiologically based pharmacokinetic model: PBPK, and toxicokinetic-toxicodynamic model: TKTD) in our understanding of the key questions in metal ecotoxicology in aquatic systems, i.e., bioaccumulation, transport and toxicity. All the models rely on the first-order kinetics principle of metal uptake and elimination. The BK model basically treats organisms as a single compartment, and is both physiologically and geochemically based. With a good understanding of each kinetic parameter, bioaccumulation of metals in any aquatic organisms can be studied holistically and mechanistically. Modeling efforts are not merely restrained from the prediction of metal accumulation in the tissues, but instead provide the direction of the key processes that need to be addressed. PBPK is more physiologically based since it mainly addresses the transportation, transformation and distribution of metals in the organisms. It can be treated conceptually as a multi-compartmental kinetic model, whereas the physiology is driving the development of any good PBPK model which is no generic for aquatic animals and contaminants. There are now increasingly applications of the PBPK modeling specifically in metal studies, which reveal many important processes that are impossible to be teased out by direct experimental measurements without adequate modeling. TKTD models further focus on metal toxicity in addition to metal bioaccumulation. The TK part links exposure and bioaccumulation, while the TD part links bioaccumulation and toxic effects. The separation of TK and TD makes it possible to model processes, e.g., toxicity modification by environmental factors, interaction between different metals, at both the toxicokinetic and toxicodynamic levels. TKTD models provide a framework for making full use of metal toxicity data, and thus provide more information for environmental risk assessments. Overall, the three models reviewed here will continue to provide guiding principles in our further studies of metal bioaccumulation and toxicity in aquatic organisms.

Predicting the survival of zebrafish larvae exposed to fluctuating pulses of lead and cadmium

Aquatic organisms are often exposed to time-varied concentrations of contaminants due to pulsed inputs in natural water. Traditional toxicology experiments are usually carried out in a constant exposure pattern, which is inconsistent with the actual environment. In this study, a refined toxicokinetic-toxicodynamic (TK-TD) model was used to study the toxic effects of Pb and Cd on zebrafish larvae under three pulse exposures with 2, 4, and 6 h, respectively. The parameter sensitivity analysis showed that J_{M, max} had the greatest impact on the output of the model. Cd or Pb pulse exposure resulted in less death than constant exposure at the same time-weighted average (TWA) concentrations. Survival fraction in larvae under 6 h interval between two pulses of Pb or Cd was larger than that under 2 h and 4 h interval. Toxicity under constant exposure of Cd or Pb was greater than that under 2, 4, and 6 h interval pulse exposure because the cumulative Cd or Pb concentration in the body under constant exposure was greater than that under pulse exposure. The results also showed that the stochastic death (SD) model was more suitable than the individual tolerance (IT) model for predicting the survival fraction of larvae under pulse exposure to Pb and Cd, which was indicated by higher R² (0.670-0.940) in SD model than that (0.588-0.861) in IT model. Our model provides approaches for laboratory toxicity testing and modeling approaches for addressing the toxicity of heavy metal pulsed exposure.

Predicting the toxic effects of Cu and Cd on Chlamydomonas reinhardtii with a DEBtox model

Predicting the sublethal effects of pollutants to aquatic organism is essential in realistic chemical risk assessment. However, only a few dynamics models for sublethal endpoints are available. Here, we investigated the toxic effects of the essential metal Cu and the nonessential metal Cd on Chlamydomonas reinhardtiiunder both single and combined exposure, compared the effectiveness of different effect endpoints as toxic effect factors, and developed a Dynamic Energy Budget toxicology (DEBtox) model to predict the sublethal effects of Cu and Cd on C. reinhardtii. The results showed that the chlorophyll fluorescence parameter is a better toxic effect indicator than others for short-term exposure (<24 h), while algal cell growth is preferred for long-term exposure (2-6 days). The developed DEBtox model could successfully predict single metal toxicity to C. reinhardtii, while the combined metal DEBtox model slightly overestimates the joint toxicity of Cu-Cd due to the antagonistic effect of Cu-Cd on C. reinhardtii. This study is helpful to understanding and better predictions of metal sublethal toxic effects on aquatic organisms.

Phytotoxicity of individual and binary mixtures of rare earth elements (Y, La, and Ce) in relation to bioavailability

Rare earth elements (REEs) are typically present as mixtures in the environment, but a quantitative understanding of mixture toxicity and interactions of REEs is still lacking. Here, we examined the toxicity to wheat (Triticum aestivum L.) of Y, La, and Ce when applied individually and in combination. Both concentration addition (CA) and independent action (IA) reference models were used for mixture toxicity analysis because the toxicity mechanisms of REEs remain obscure. Upon single exposure, the EC50s of Y, La, and Ce, expressed as dissolved concentrations, were 1.73 ± 0.24 μM, 2.59 ± 0.23 μM, and 1.50 ± 0.22 μM, respectively. The toxicity measured with relative root elongation followed La < Y ≈ Ce, irrespective of the dose descriptors. The use of CA and IA provided similar estimates of REE mixture interactions and toxicity. When expressed as dissolved metal concentrations, nearly additive effects were observed in Y-La and La-Ce mixtures, while antagonistic interactions were seen in Y-Ce mixtures. When expressed as free metal activities, antagonistic interactions were found for all three binary mixtures. This can be explained by a competitive effect of REEs ions for binding to the active sites of plant roots. The application of a more elaborate MIXTOX model in conjunction with the free ion activities, which incorporates the non-additive interactions and bioavailability-modifying factors, well predicted the mixture toxicity (with >92% of toxicity variations explained). Our results highlighted the importance of considering mixture interactions and subsequent bioavailability in assessing the joint toxicity of REEs.

Development of a new method for biomonitoring of multiple metals in occupational exposure

The assessment of co-exposure to several types of metal contamination poses a hurdle for occupational monitoring. Determination of elements in biological samples is an important way to evaluate occupational exposure. However, optimized methods for the extraction of multiple metals from biological samples have not been reported in recent studies. Therefore, solid-phase extraction (SPE) based on the functionalized nano-zeolite Y was suggested for the biomonitoring of metal co-exposure. SPE was conducted with ammonium pyrrolidine dithiocarbamate (APDC) surrounded by Triton X-100 micelles, which were loaded into the pores of nano-zeolite Y. In this study, SPE was optimized for pre-concentration of trace amounts of chromium (Cr), cadmium (Cd), and lead (Pb) in urine samples with respect to the pH, APDC concentration, elution condition, amount of functionalized nano-zeolite Y, and sample volume. This method has been successfully optimized for the extraction of the mentioned multiple metals with >97% efficiency and an acceptable reproducibility with a coefficient variation of <10%. This method could be used in the extraction of multiple metals in environmental and occupational exposure conditions. In this study, urine samples of welding workers were evaluated following this optimized method.

Predicting cadmium and lead toxicities in zebrafish (Danio rerio) larvae by using a toxicokinetic-toxicodynamic model that considers the effects of cations

Protons and cations may affect metal accumulation in aquatic organisms and further influence metal toxicity. The effects of K⁺, Na⁺, Ca²⁺, Mg²⁺, and H⁺ on the accumulation and toxicity of Cd and Pb in zebrafish larvae after 24 h exposure were examined. We found that Na⁺, Ca²⁺, Mg²⁺, and H⁺ exerted significant effects on both the accumulation and toxicity of Cd, and Ca²⁺, Mg²⁺, and H⁺ also affected both the accumulation and toxicity of Pb significantly. Subsequently, stability constants for the binding of Pb²⁺, Cd²⁺, K⁺, Ca²⁺, Mg²⁺, Na⁺, and H⁺ to biotic ligand were estimated with the Langmuir model and biotic ligand model (BLM). Using the BLM-estimated binding constants calculated with toxicity data, a refined toxicokinetic-toxicodynamic (TK-TD) model considering cation competition effects was used to predict Cd and Pb accumulation and survival rates in zebrafish larvae with varying cation concentrations. Results showed that the developed TK-TD model could successfully predict Cd and Pb toxicity to zebrafish larvae as a function of major competitive cations. The TK-TD model incorporated cation competition effects is a promising tool to quantify and assess the metal risk in natural water.

Development of multi-metal interaction model for Daphnia magna: Significance of metallothionein in cellular redistribution

Despite the great progress made in metal-induced toxicity mechanisms, a critical knowledge gap still exists in predicting adverse effects of heavy metals on living organisms in the natural environment, particularly during exposure to multi-metals. In this study, a multi-metal interaction model of Daphnia manga was developed in an effort to provide reasonable explanations regarding the joint effects resulting from exposure to multi-metals. Metallothionein (MT), a widely used biomarker, was selected. In this model, MT was supposed to play the role of a crucial transfer protein rather than detoxifying protein. Therefore, competitive complexation of metals to MT could highly affect the cellular metal redistribution. Thus, competitive complexation of MT in D. magna with metals like Pb²⁺, Cd²⁺ and Cu²⁺ was qualitatively studied. The results suggested that Cd²⁺ had the highest affinity towards MT, followed by Pb²⁺ and Cu²⁺. On the other hand, the combination of MT with Cu²⁺ appeared to alter its structure which resulted in higher affinity towards Pb²⁺. Overall, the predicted bioaccumulation of metals under multi-metal exposure was consisted with earlier reported studies. This model provided an alternative angle for joint effect through a combination of kinetic process and internal interactions, which could help to develop future models predicting toxicity to multi-metal exposure.

Quantifying the interactions among metal mixtures in toxicodynamic process with generalized linear model

Predicting the toxicity of chemical mixtures is difficult because of the additive, antagonistic, or synergistic interactions among the mixture components. Antagonistic and synergistic interactions are dominant in metal mixtures, and their distributions may correlate with exposure concentrations. However, whether the interaction types of metal mixtures change at different time points during toxicodynamic (TD) processes is undetermined because of insufficient appropriate models and metal bioaccumulation data at different time points. In the present study, the generalized linear model (GLM) was used to illustrate the combined toxicities of binary metal mixtures, such as Cu-Zn, Cu-Cd, and Cd-Pb, to zebrafish larvae (Danio rerio). GLM was also used to identify possible interaction types among these method for the traditional concentration addition (CA) and independent action (IA) models. Then the GLM were applied to quantify the different possible interaction types for metal mixture toxicity (Cu-Zn, Cu-Cd, and Cd-Pb to D. rerio and Ni-Co to Oligochaeta Enchytraeus crypticus) during the TD process at different exposure times. We found different metal interaction responses in the TD process and interactive coefficients significantly changed at different exposure times (p<0.05), which indicated that the interaction types among Cu-Zn, Cu-Cd, Cd-Pb and Ni-Co were time dependent. Our analysis highlighted the importance of considering joint actions in the TD process to understand and predict metal mixture toxicology on organisms. Moreover, care should be taken when evaluating interactions in toxicity prediction because results may vary at different time points. The GLM could be an alternative or complementary approach for BLM to analyze and predict metal mixture toxicity.

Concentration addition and independent action model: Which is better in predicting the toxicity for metal mixtures on zebrafish larvae

The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures.

Dissolved organic matter affects the bioaccumulation of copper and lead in Chlorella pyrenoidosa: A case of long-term exposure

This study evaluated the impact of dissolved organic matter (DOM) of varying molecule weights (MWs) on long-term exposure to Cu and Pb in Chlorella pyrenoidosa. Citric acid, fulvic acid, and humic acid, in the order of increasing MWs, were selected to represent DOM. The results showed that DOM with larger MWs had stronger inhibitory effects on the bioavailability of Cu to algae. However, the biosorption isotherm of Pb in the presence of DOM was different: as Pb equilibrium concentration increased, the biosorption capacity increased sharply to a maximum, then decreased. The maximum values ranged between 0.186 and 0.398 mmol g^-1, as the solution DOM concentration and MW changed, exhibiting a stoichiometric relationship between DOM, Pb and algae. The ternary complex of Pb-DOM-alga formed in a limited Pb concentration range, and increased the percentage of internalized Pb. This research helps to understand the role of DOM in metal uptake in phytoplankton.

Toxicodynamic modeling of zebrafish larvae to metals using stochastic death and individual tolerance models: comparisons of model assumptions, parameter sensitivity and predictive performance

Process-based toxicodynamic (TD) models are playing an increasing role in predicting chemical toxicity to aquatic organism. Stochastic death (SD) and individual tolerance distribution (IT) are two often used assumptions in TD models which could lead to different consequences for risk assessment of chemicals. Here, using the toxicity data of single (Cu, Zn, Cd, and Pb) and their binary metal mixtures on survival of zebrafish larvae, we assessed the parameter sensitivity and evaluated the predictive performance of SD and IT models. The sensitivity analysis indicated the parameters related to toxicodynamics such as k _k and threshold, had a great influence on the SD model's output and α had a great influence on the IT model's output. The predicted survival probability was highly sensitive to the assumptions of SD or IT models, and the SD model explained toxicity of single metal and binary metal mixtures better than IT model. Our results suggested that SD model is more suitable in assessing the metal toxicity to zebrafish larvae. Moreover, different combinations of laboratory metal-specific and species-specific experiments with SD and IT models need further study for better understanding and predicting toxic effects for different metals and organisms.

Modeling interactions and toxicity of Cu-Zn mixtures to zebrafish larvae

Quantitative predictions of metal-metal interactions and toxicity in aquatic organisms meet a unique challenge. Accumulation and toxicity of Cu and Zn mixtures in zebrafish larvae has been quantified in binary metal system with variable combinatorial concentrations in order to understand the interactions between essential trace metals and assess availability of the toxicokinetic-toxicodynamic (TK-TD) model which simulated the uptake of metals over time as well as metal toxicity after 24h of exposure. Competitive uptake experiments showed a straightforward antagonistic competition, as would be predicted by Michaelis-Menten competitive equilibrium model. Zn uptake decreased significantly in the presence of Cu²⁺ concentrations higher than 10^-6M. Cu²⁺ was shown to compete strongly with Zn for uptake, having a higher affinity constant to biotic ligand (BL) sites (K_CuBL=10^5.42M^-1) than Zn (K_ZnBL=10^4.13M^-1). TK-TD model considering potential metal-metal antagonism interactions showed good predictive power in predicting accumulation and toxicity of Cu-Zn mixtures in zebrafish larvae with the high coefficient of determination (r²) and significant level (p). In particular, with the elevated Zn concentrations in mixtures, the TD model showed better predictive power in predicting toxicity of 10^-6M Cu concentration in Cu-Zn mixtures. The TK-TD analysis provided some new insights into the interactive mechanism of binary Cu and Zn exposure in aquatic animals and may have important implications for our understanding of quantitative predictions of metal-metal interactions and toxicity in a field where animals are simultaneously exposed to several metals.

Internal concentration as a better predictor of metal toxicity than the fractional coverage of metals on biotic ligand: Comparison of 3 modeling approaches

Modeling toxicity of metal mixtures poses unique challenges to the incorporation of bioavailability and metal speciation in metal exposures. Three models (models I, II, and III) were compared in the present study to predict and interpret the toxicity exerted by binary metal mixtures to zebrafish larvae, with the assumption of competition between metals based on the biotic ligand model and toxic potencies of individual metals. In addition, 3 models were developed by substituting binding constants (f_MBL ) for internal metal concentrations (C_M,int ) to directly delineate single-metal and mixture effects on mortality of zebrafish larvae. The results indicated that the 3 developed models appeared to be much better (p < 0.01) than 3 previous models at assessing the toxicity of different metal mixtures and showed 10% to 20% predictive improvement for each metal combination, with the toxic equivalency factor-based model II showing the best performance at quantifying metal mixture toxicity. The 3 developed models generally provided a reasonable framework and descriptions of bioavailability and additive (or nearly additive) toxicity for a number of binary metal mixtures. Environ Toxicol Chem 2016;35:2721-2733. © 2016 SETAC.

Risk assessment of environmental mixture effects

Determining interactions of multi-component environmental mixtures towards accurate risk assessment.