Mixture cytotoxicity assessment of ionic liquids and heavy metals in MCF-7 cells using mixtox.

Ionic liquids (ILs) are widely used as extractants for heavy metals. However, the effect of mixtures of ILs and heavy metals is rarely understood. In this study, we tested the cytotoxicity of four ILs, four heavy metals and their mixtures on human MCF-7 cells in 96-well microplates. The toxicity of single compounds in MCF-7 cells ranges from 3.07 × 10(-6) M for Cu(II) to 2.20 × 10(-3) M for 1-ethyl-3-methylimidazolium tetrafluoroborate. The toxicity of heavy metals in MCF-7 is generally higher than the toxicity of ILs. A uniform experimental design was used to simulate environmentally realistic mixtures. Two classical reference models (concentration addition and independent action) were used to predict their mixture. The experiments to evaluate the toxicity of the mixture revealed antagonism among four ILs and four heavy metals in MCF-7 cells. Pearson correlation analysis showed that Ni(II) and 1-dodecyl-3-methylimidazolium chloride are positively correlated with the extent of antagonism, while 1-hexyl-3-methylimidazolium tetrafluoroborate showed a negative correlation. Data analysis was conducted in the R package mixtox, which integrates features such as curve fitting, experimental design, and mixture toxicity prediction. The international community of toxicologists is welcome to use this package and provide feedback as suggestions and comments.

Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO.

Variable selection is of crucial significance in QSAR modeling since it increases the model predictive ability and reduces noise. The selection of the right variables is far more complicated than the development of predictive models. In this study, eight continuous and categorical data sets were employed to explore the applicability of two distinct variable selection methods random forests (RF) and least absolute shrinkage and selection operator (LASSO). Variable selection was performed: (1) by using recursive random forests to rule out a quarter of the least important descriptors at each iteration and (2) by using LASSO modeling with 10-fold inner cross-validation to tune its penalty λ for each data set. Along with regular statistical parameters of model performance, we proposed the highest pairwise correlation rate, average pairwise Pearson's correlation coefficient, and Tanimoto coefficient to evaluate the optimal by RF and LASSO in an extensive way. Results showed that variable selection could allow a tremendous reduction of noisy descriptors (at most 96% with RF method in this study) and apparently enhance model's predictive performance as well. Furthermore, random forests showed property of gathering important predictors without restricting their pairwise correlation, which is contrary to LASSO. The mutual exclusion of highly correlated variables in LASSO modeling tends to skip important variables that are highly related to response endpoints and thus undermine the model's predictive performance. The optimal variables selected by RF share low similarity with those by LASSO (e.g., the Tanimoto coefficients were smaller than 0.20 in seven out of eight data sets). We found that the differences between RF and LASSO predictive performances mainly resulted from the variables selected by different strategies rather than the learning algorithms. Our study showed that the right selection of variables is more important than the learning algorithm for modeling. We hope that a standard procedure could be developed based on these proposed statistical metrics to select the truly important variables for model interpretation, as well as for further use to facilitate drug discovery and environmental toxicity assessment.

Two-stage prediction of the effects of imidazolium and pyridinium ionic liquid mixtures on luciferase.

The predicted toxicity of mixtures of imidazolium and pyridinium ionic liquids (ILs) in the ratios of their EC50, EC10, and NOEC (no observed effect concentration) were compared to the observed toxicity of these mixtures on luciferase. The toxicities of EC50 ratio mixture can be effectively predicted by two-stage prediction (TSP) method, but were overestimated by the concentration addition (CA) model and underestimated by the independent action (IA) model. The toxicities of EC10 ratio mixtures can be basically predicted by TSP and CA, but were underestimated by IA. The toxicities of NOEC ratio mixtures can be predicted by TSP and CA in a certain concentration range, but were underestimated by IA. Our results support the use of TSP as a default approach for predicting the combined effect of different types of ILs at the molecular level. In addition, mixtures of ILs mixed at NOEC and EC10 could cause significant effects of 64.1% and 97.7%, respectively. Therefore, we should pay high attention to the combined effects in mixture risk assessment.

Predicting synergistic toxicity of heavy metals and ionic liquids on photobacterium Q67.

Results from three mathematical approaches to predict the toxicity of uniform design mixtures of four heavy metals (HMs) including Cd(II), Ni(II), Cu(II), and Zn(II) and six ionic liquids (ILs) were compared to the observed toxicity of these mixtures on Vibrio qinghaiensis sp.-Q67. Single toxicity analysis indicated that the ILs had greater toxicity than the HMs. Combined toxicities of HMs and ILs were found to be synergistic. The combined toxicities were underestimated by concentration addition (CA) and independent action (IA) models. However, the mixture toxicities were effectively predicted by the integrated CA with IA based on multiple linear regression model (ICIM). We propose that ICIM model can serve as a useful tool for predicting the toxicity of interactive mixtures.

[Microplate luminometry for toxicity bioassay of chemicals on luciferase].

A new microplate luminometry for the toxicity bioassay of chemicals on firefly luciferase, was developed using the multifunctional microplate reader (SpectraMax M5) to measure the luminous intensity of luciferase. Efects of luciferase concentration, luciferin concentration, ATP concentration, pH, temperature, and reaction time on the luminescence were systematically investigated. It was found that ATP exerted a biphasic response on the luciferase luminescence and the maximum relative light units (RLU) occurred at an ATP concentration of 1.1 x 10(-4) mol x L(-1). The method was successfully employed in the toxic effect test of NaF, NaCl, KBr and NaBF4 on luciferase. Using nonlinear least square technique, the dose-response curves (DRC) of the 4 chemicals were accurately fitted with the coefficient of determination (R2) between the fitted and observed responses being greater than 0.99. The median effective concentration (EC50) of the 4 chemicals were accurately measured from the DRC models. Compared with some literatures, the bioassay is a fast easy-operate and cost-effective method with high accuracy.

Predicting mixture toxicity of seven phenolic compounds with similar and dissimilar action mechanisms to Vibrio qinghaiensis sp.nov.Q67.

The predictions of mixture toxicity for chemicals are commonly based on two models: concentration addition (CA) and independent action (IA). Whether the CA and IA can predict mixture toxicity of phenolic compounds with similar and dissimilar action mechanisms was studied. The mixture toxicity was predicted on the basis of the concentration-response data of individual compounds. Test mixtures at different concentration ratios and concentration levels were designed using two methods. The results showed that the Weibull function fit well with the concentration-response data of all the components and their mixtures, with all relative coefficients (Rs) greater than 0.99 and root mean squared errors (RMSEs) less than 0.04. The predicted values from CA and IA models conformed to observed values of the mixtures. Therefore, it can be concluded that both CA and IA can predict reliable results for the mixture toxicity of the phenolic compounds with similar and dissimilar action mechanisms.

Predicting hormetic effects of ionic liquid mixtures on luciferase activity using the concentration addition model.

The concept of hormesis has generated considerable interest within the environmental and toxicological communities over the past decades. However, toxicological evaluation and prediction of hormesis in mixtures are challenging and only just unfolding. The hormetic effects of ten ionic liquids (ILs), singly and in mixtures in the ratios of their individual EC50, EC10, EC0, and ECm (maximal stimulatory effect concentration), on luciferase luminescence were determined by using microplate toxicity analysis. There was good agreement between the effects observed and predicted by concentration addition (CA) for all four mixtures. This evidence supports the use of CA model as a default approach for assessing the combined effect of chemicals at the molecular level. Focusing on the selected points of the concentration-response curves (CRCs) of mixtures, the mixtures of IL chemicals mixed at concentrations that individually showed stimulatory effects could produce inhibitory or no effects, and the mixture of IL chemicals mixed at concentrations that individually showed no effects could produce significant inhibitory effect. The three interesting phenomena in mixture hormesis may have important implications for current risk assessment practices.

Comparison between the short-term and the long-term toxicity of six triazine herbicides on photobacteria Q67.

The bioluminescence inhibition of six triazine herbicides including desmetryne (DES), simetryn (SIM), velpar (VEL), prometon (PRO), metribuzin (MET), and aminotriazine (AMI) on Vibrio qinghaiensis sp.-Q67 (Q67) was determined to investigate the effects of exposure duration on the ecotoxicological relevance of triazine herbicides. Based on the short-term microplate toxicity analysis (MTA), a long-term MTA was established to assess the impact of exposure time on the toxicities of the herbicides. The results show that the long-term toxicities of DES and SIM are similar to their short-term toxicities, and the long-term toxicities of VEL, PRO, and MET are higher than their short-term toxicities, while AMI without short-term toxicity has a high long-term toxicity. In addition, a parabolic relationship was found between the pEC(50) (the negative logarithm of the EC(50), log 1/EC(50)) and the logarithm of octanol-water partition coefficient (logK(ow)). To better understand their toxicity process, the time-dependent toxicities of the six herbicides on Q67 were determined over a period of 12 h during which measurements were taken every 30 min to generate an integral effect surface related to both concentration and duration.

Prediction for the mixture toxicity of six organophosphorus pesticides to the luminescent bacterium Q67.

Organophosphorus (OP) pesticides are ubiquitous in the surface water as mixtures. To examine the mixture toxicity in the multi-component space, the uniform design (UD) which can explore the concentration changes with few experimental efforts was employed to design the mixtures. On the other hand, the fixed concentration ratio ray was applied into six UD mixtures and two equivalent-effect concentration mixtures to build the whole concentration-response curves to overcome the demerit of the classical "point-to-point" method. The experimental toxicities of six pesticides and their mixtures to the luminescent bacterium Q67 were determined. The mixture toxicities were predicted by two models, concentration addition (CA) and independent action (IA). The results showed that all the mixture toxicities observed had no significant differences from the ones predicted by CA. However, the mixture toxicities were also well predicted by IA especially at the low-concentration section.

A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV).

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient (r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient (q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133-145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87-97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups >C= and -CH=. 58 PCBs were divided into an "odd set" and "even set" in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for "odd set", and ME for "even set", can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.