Time-dependence in mixture toxicity prediction

New models for the time dependent toxicity of individual and combined toxicants

Time-dependent toxicity data of specific toxicants observed against A. fischeri were analysed using a single time-dependent Weibull cumulative distribution function (CDF). For binary mixtures the individual Weibull parameters provided the marginals for a bivariate copula model, characterised by a single extra parameter, the interaction exponent, θ. The copula model unites both the dose addition (DA, Loewe additivity) and dose independence (DI, Bliss independence) hypotheses of combinations into a single explicit equation and returns both hypotheses as special cases. The model predicts the linear isoboles from sham (like against like) experiments and the linear, concave, convex and mixed concave-convex isoboles from true binary mixtures. Systems are defined as being independent when θ = 1, additive when min(n 1, n 2) ≤ θ ≤ max(n 1, n 2), antagonistic when θ < min(n 1, n 2) and synergistic when θ > max(n 1, n 2), where n i are the individual dose (concentration) exponents. More complex mixtures were analysed by developing n-dimensional copulas: two ternary systems were analysed using a ternary copula that returned the three bivariate marginals and the three individual marginals of the mixture. The general model can also be used for time-independent studies by simply removing the time dependency. Research into combined effects has often assumed that if both the parameters for all the individual components in a mixture and the model for combinations were known then the additive effect of the whole could be predicted. This hypothesis has been shown to be false because without knowing how the components in a mixture interact the predictions from the standard DI or DA models provide only an initial, best guess, analysis.

Evaluation of consistency for multiple experiments of a single combination in the time-dependence mixture toxicity assay

Test-to-test consistency was evaluated for a single binary combination of organic chemicals using an assay that examined toxicity over multiple exposure times. Six experiments were conducted. The toxicities of 3-chloro-2-butanone (3C2B), methyl crotonate (MC) and the mixture of both (MX) were evaluated in each experiment at 15, 30 and 45 min of exposure using the Microtox^® system. Concentration-response (x/y) curves were generated via the five-parameter logistic minus one-parameter (5PL - 1P) curve-fitting function and were used to develop predicted x/y curves for the dose-addition (DA) and independence (I) models of combined effect. A variety of toxicity (e.g. effective concentration, EC₅₀) and time-dependent toxicity (TDT) endpoints, 5PL - 1P parameters and various combined-effects metrics (e.g. MX/DA) were calculated. Test-to-test consistency was evaluated via the coefficient of variation (CV) or, for TDT, the standard deviation of mean values. In the study, CVs obtained for single-chemical and mixture toxicity endpoints (EC₂₅, EC₅₀ and EC₇₅) at each exposure time were <20, as were those for the predicted DA and I curves. For the MX/DA metric, mixture toxicity was consistent with that predicted for DA at each exposure time in each experiment with CVs <6, despite some substantial variation in TDT for MC-alone at the EC₂₅ and for the 30-45 min time-interval. There was a lower variation in TDT for 3C2B and MX. Mean and CV values for 5PL - 1P-derived slope and asymmetry parameters were also assessed to provide bases for comparisons in future reports.

(Q)SARs to predict environmental toxicities: current status and future needs

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

Evaluation of time-dependent toxicity and combined effects for a series of mono-halogenated acetonitrile-containing binary mixtures

Mixture and time-dependent toxicity (TDT) was assessed for a series of mono-halogenated acetonitrile-containing combinations. Inhibition of bioluminescence in Aliivibrio fischeri was measured after 15, 30 and 45-min of exposure. Concentration-response (x/y) curves were determined for each chemical alone at each timepoint, and used to develop predicted x/y curves for the dose-addition and independence models of combined effect. The x/y data for each binary mixture was then evaluated against the predicted mixture curves. Two metrics of mixture toxicity were calculated per combined effect model: (1) an EC₅₀-based dose-addition (AQ) or independence (IQ) quotient and (2) the mixture/dose-addition (MX/DA) and mixture/independence (MX/I) metrics. For each single chemical and mixture tested, TDT was also calculated. After 45-min of exposure, 25 of 67 mixtures produced curves that were consistent with dose-addition using the MX/DA metric, with the other 42 being less toxic than predicted by MX/DA. Some mixtures had toxicity that was consistent with both dose-addition and independence. In general, those that were less toxic than predicted for dose-addition were also less toxic than predicted for independence. Of the 25 combinations that were consistent with dose-addition, 22 (88%) mixtures contained chemicals for which the individual TDT values were both >80%. In contrast, of the 42 non-dose-additive combinations, only 2 (4.8%) of the mixtures had both chemicals with individual TDT values >80%. The results support previous findings that TDT determinations can be useful for predicting chemical mixture toxicity.