Antibiotics as a type of environmental contaminants are typically exposed to chemical mixtures over long periods of time, so chronic combined toxicity is the best way to perform an environmental risk assessment. In this paper, the individual and combined toxicity of sulfonamides (SAs), sulfonamide potentiators (SAPs), and doxycycline hyclate (DH) were tested on gram-positive (Bacillus subtilis, B. subtilis) and gram-negative (Escherichia coli, E. coli) bacteria. The individual toxicity of antibiotics on the two bacteria could be ranked in the same order: SAs < SAPs < DH. But E. coli was more sensitive than B. subtilis to the antibiotics, which was likely due to both the different abilities of antibiotics to pass through the cell membrane and the varied capacities to bind target proteins between the two bacteria. In addition, the binary mixtures of SAs–SAPs, SAs–DH, and SAs–SAs exhibited synergistic, antagonistic, and additive effects on both of the bacteria but in different magnitudes as represented by the toxicity units (TU). And we found the different TU values were result from the different effective concentrations of antibiotic mixtures based on the approach of molecular docking and quantitative structure–activity relationships (QSARs). Moreover, from the results of risk assessment, it should be noted that the mixture of SAs and other antibiotics may pose a potential environmental risk assessment due to their combined action with the current environmentally realistic concentrations.

As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure–activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R2) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.

During the past two decades, the phenomenon of hormesis has gained increasing recognition in environmental and toxicological communities. However, the mechanistic understanding of hormesis, to date, is extremely limited. Herein is proposed a novel parametric model with a mechanistic basis and two model-based parameters for hormesis that was successfully applied to the hormetic dose–response observed in the chronic toxicity of sulfonamides on Photobacterium phosphoreum. On the basis of the methods of molecular docking and quantitative structure–activity relationships (QSARs), we proposed a mechanistic hypothesis for hormesis that introduces for the first time the concept of quorum sensing in toxicological studies and explains the mechanism at the level of the receptors. The mechanistic hypothesis stated that (1) specific target binding like interaction with LuxR may contribute to transcriptional activation leading to enhanced luciferase activity at low dose exposure of sulfonamides, and (2) as the dose of sulfonamides increases, more sulfonamides competitively bind to dihydropteroate synthase, which inhibit the biosynthesis of folic acid and thus provoke toxicity. This mechanistic hypothesis, which explains both the dose-dependent and time-dependent features of hormesis, could give new insight into the mechanistic study of hormesis.

Although environmental contaminants are usually encountered as nonequitoxic mixtures, most studies have investigated the toxicity of equitoxic mixtures. In the present study, a method for prediction of the toxicity of nonequitoxic mixtures was developed using the similarity parameter (λ). The joint effect of multiple contaminants at the median inhibition concentration in equitoxic (\( {\text{TU}}_{\text{sum-50}}^{\text{equitoxic}} \)) and nonequitoxic (\( {\text{TU}}_{\text{sum-50}}^{\text{nonequitoxic}} \)) binary, ternary, and quaternary mixtures was investigated using Vibrio fischeri. The observed results indicate that the concentration ratios of individual chemicals in the mixtures influenced the joint effects, and that λ could be employed to evaluate the relation between \( {\text{TU}}_{\text{sum-50}}^{\text{equitoxic}} \) and \( {\text{TU}}_{\text{sum-50}}^{\text{nonequitoxic}} \). Prediction models for the joint effects of nonequitoxic (\( {\text{TU}}_{\text{sum-50}}^{\text{nonequitoxic}} \)) mixtures were derived from a combination of \( {\text{TU}}_{\text{sum-50}}^{\text{equitoxic}} \) and λ. The predictive capabilities of these models were validated by comparing the predicted data with the observed data for binary, ternary, and quaternary mixtures. The prediction models have promising applications in controlling environmental pollution, evaluating drug interactions, and optimizing combinations of pesticides used in agriculture.

In this study, a total of 40 tests of the toxicity of 8 halogenated benzenes to five algal species were performed. The result demonstrated that the toxicity of halogenated benzenes to five algal species was directly related to the hydrophobicity of the chemicals and the lipid content of the algae. Based on the results, we developed a Kow-based quantitative structure–activity relationship (QSAR) model: log(1/EC50) = 1.050logKow + 1.429log(1/lipid)−3.224 with n = 40, r2 = 0.946, S.E. = 0.211, F = 323.933 at p < 0.001. This model provides evidence that the toxicity of halogenated benzenes to these five algae tested is related to the slower transference into lipid. This model can potentially be generalized to other algal species and toxicants.