Prediction of Synergistic Toxicity of Binary Mixtures to Vibrio fischeri Based on Biomolecular Interaction Networks

Predicting the occurrence of antagonism within ternary guanidine mixture pollutants based on the concentration ratio of components

The widespread prevalence and coexistence of diverse guanidine compounds pose substantial risks of potential toxicity interactions, synergism or antagonism, to environmental organisms. This complexity presents a formidable challenge in assessing the risks associated with various pollutants. Hence, a method that is both accurate and universally applicable for predicting toxicity interactions within mixtures is crucial, given the unimaginable diversity of potential combinations. A toxicity interaction prediction method (TIPM) developed in our past research was employed to predict the toxicity interaction, within guanidine compound mixtures. Here, antagonism were found in the mixtures of three guanidine compounds including chlorhexidine (CHL), metformin (MET), and chlorhexidine digluconate (CDE) by selecting Escherichia coli (E. coli) as the test organism. The antagonism in the mixture was probably due to the competitive binding of all three guanidine compounds to the anionic phosphates of E. coli cell membranes, which eventually lead to cell membrane rupture. Then, a good correlation between toxicity interactions (antagonisms) and components' concentration ratios (pis) within binary mixtures (CHL-MET, CHL-CDE, MET-CDE) was established. Based on the correlation, the TIPM was constructed and accurately predicted the antagonism in the CHL-MET-CDE ternary mixture, which once again proved the accuracy and applicability of the TIPM method. Therefore, TIPM can be suggested to identify or screen rapidly the toxicity interaction within ternary mixtures exerting potentially adverse effects on the environment.

A novel method for predicting the emergence of toxicity interaction in ternary mixtures

The environment is teeming with a wide variety of pollutants, but the complexity and diversity of their combinations make it difficult to fully assess their toxicity interaction. A novel toxicity interaction prediction method (TIPM) based on the three-dimensional (3D) surface form of the concentration addition (CA) deviation model (dCA) was proposed to predict the emergence of toxicity interaction in ternary mixtures. Doxycycline hyclate (DH), bromoacetic acid (BAA) and iodoacetic acid (IAA) were used as target pollutants. The toxicity of binary and ternary mixtures designed by the direct equipartition ray design method (EquRay) and the uniform design ray method (UD-Ray) against Escherichia coli (E. coli) was determined by using a time-dependent microplate toxicity analysis (t-MTA) method. The toxicity interaction within mixtures was characterized qualitatively and quantitatively using dCA 3D surface modeling and the emergence of DH-MAA-IAA toxicity interaction was predicted by TIPM. The results showed that the dCA 3D surface model could well characterize the toxicity interactions of the mixtures, and toxicity interaction was closely related to the components' concentration ratio (pi). TIPM could predict the emergence of DH-MAA-IAA toxicity interactions well based on the relationship. Due the model is only related to the toxicity interactions and pi value of a mixture, so it can be suggested to predict toxicity interaction within the more complex multicomponent mixtures, which provides a novel approach for the environmental risk assessment and prediction of hazardous substances.

Ecotoxicity and interacting mechanism of anionic surfactant sodium dodecyl sulfate (SDS) and its mixtures with nonionic surfactant fatty alcohol-polyoxyethlene ether (AEO)

This paper reports the proportion-dependent toxicity of binary surfactant mixtures containing anionic sodium dodecyl sulfate (SDS) and nonionic fatty alcohol-polyoxyethlene ether (AEO) toward Photobacterium phosphoreum. The crucial role of toxicity interactions was elucidated by spectroscopic probing the refolding of the unfolded bovine serum albumin (BSA) induced by SDS and theoretical calculating the interaction parameter of mixed surfactants based on Rubingh's model from the critical micelle concentrations. The SDS/AEO mixtures can be divided into two groups based on the toxicity response to the proportion of AEO in the mixtures: Group I contained low mass proportions of AEO, that is, SDS:AEO = 4:1, 3:1; Group II featured high AEO proportions, that is, SDS:AEO = 3:2, 1:1, 2:3, 1:4. The toxicity of SDS/AEO mixtures decreased with the enhanced proportion of AEO in Group I and then fluctuated slightly when the AEO proportion increased to that of Group II. The mixture with the mass ratio of 1:1 showed a slightly higher toxicity than the others in Group II. Scanning electron microscopy (SEM) images illustrated that the addition of AEO hindered the action of SDS against the cell membrane. Fluorescence measurement indicated that AEO could extract SDS molecules embedded in the BSA matrix, except for those bound to the highly active sites of BSA, and refold stepwise the unfolded protein. The results were in excellent analogy to the proportion-dependent toxicity of SDS/AEO mixture, indicating the formation of mixed micelles playing a key role. The interaction parameter further revealed that antagonism led to the mixture with equal mass ratio (1:1) showing higher toxicity than other mass ratios in Group II. These results can be useful for compounding SDS/AEO mixtures in application efficiently and eco-friendly.

Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models

Pollinators such as honey bees are of considerable importance, because of the crucial pollination services they provide for food crops and wild plants. Since bees are exposed to a wide range of multiple chemicals "mixtures" both of anthropogenic (e.g. plant protection products) and natural origin (e.g. plant toxins), understanding their combined toxicity is critical. Although honey bees are employed worldwide as surrogate species for Apis and non-Apis bees in toxicity tests, it is practically unfeasible to perform in vivo tests for all mixtures of chemicals. Therefore, Quantitative Structure-Activity Relationships (QSAR) models can be developed using available data and can provide useful tools to predict such combined toxicity. Here, three different QSAR models within the CORAL software have been calibrated and validated for honey bees (A. mellifera) to predict the acute contact mixtures potency (LD_50-mix), in two regression based-models, and the nature of combined toxicity (synergism / non-synergism) in a classification-based model. Experimental data on binary mixtures (n = 123) (LD_50-mix) including dose response data (n = 97) and corresponding Toxic Unit values were retrieved from EFSA databases. The models were built using the principle of extraction of attributes from SMILES (or quasi-SMILES) while calculating so-called correlation weights for these attributes using Monte Carlo techniques. The two regression models were validated for their reliability and robustness (R² = 0.89, CCC = 0.92, Q² = 0.81; R² = 0.87, CCC = 0.89, Q² = 0.75). The classification model was validated using sensitivity (=0.86), specificity (=1), accuracy (=0.96), and Matthews correlation coefficient (MCC = 0.90) as qualitative statistical validation parameters. Results indicate that these QSAR models successfully predict acute contact toxicity of binary mixtures in honey bees and can support prioritisation of multiple chemicals of concerns. Data gaps and further development of QSAR models for honey bees are highlighted particularly for chronic and sub-lethal effects.