QSAR modeling of toxicity of acyclic quaternary ammonium compounds on Scenedesmus Quadricauda using 2D and 3D descriptors

Screening for frequently detected quaternary ammonium mixture systems in waters based on frequent itemset mining and prediction of their toxicity

Screening and prioritizing research on frequently detected mixture systems in the environment is of great significance, as conducting toxicity testing on all mixtures is impractical. Therefore, the frequent itemset mining (FIM) was introduced and applied in this paper to identify variables that commonly co-occur in a dataset. Based on the dataset of the quaternary ammonium compounds (QACs) in the water environment, the four frequent QAC mixture systems with detection rate ≥ 35 % were found, including [BDMM]+Cl--[BTMM]+Cl- (M1), [BDMM]+Cl--[BHMM]+Cl- (M2), [BTMM]+Cl- -[BHMM]+Cl- (M3), and [BDMM]+Cl--[BTMM]+Cl--[BHMM]+Cl- (M4). [BDMM]+Cl-, [BTMM]+Cl-, and [BHMM]+Cl- are benzyl dodecyl dimethyl ammonium chloride, benzyl tetradecyl dimethyl ammonium chloride, and benzyl hexadecyl dimethyl ammonium chloride, respectively. Then, the toxicity of the representative mixture rays and components for the four frequently detected mixture systems was tested using Vibrio qinghaiensis sp.-Q67 (Q67) as a luminescent indicator organism at 0.25 and 12 h. The toxicity of the mixtures was predicted using concentration addition (CA) and independent action (IA) models. It was shown that both the components and the representative mixture rays for the four frequently detected mixture systems exhibited obvious acute and chronic toxicity to Q67, and their median effective concentrations (EC50) were below 7 mg/L. Both CA and IA models predicted the toxicity of the four mixture systems well. However, the CA model had a better predictive ability for the toxicity of the M3 and M4 mixtures than IA at 12 h.

A Quantitative Structure-Activity Relationship Approach to Determine Biotoxicity of Amide Herbicides for Ecotoxicological Risk Assessment

Amide herbicides have been widely applied in agriculture and found to be widespread and affect nontarget organisms in the environment. To better understand the biotoxicity mechanisms and determine the toxicity to the nontarget organisms for the hazard and risk assessment, five QSAR models were developed for the biotoxicity prediction of amide herbicides toward five aquatic and terrestrial organisms (including algae, daphnia, fish, earthworm and avian species), based on toxicity concentration and quantitative molecular descriptors. The results showed that the developed models complied with OECD principles for QSAR validation and presented excellent performances in predictive ability. In combination, the investigated QSAR relationship led to the toxicity mechanisms that eleven electrical descriptors (E_HOMO, E_LUMO, α_xx, α_yy, α_zz, μ, qN^-, Q_xx, Q_yy, qH⁺, and q^-), four thermodynamic descriptors (Cv, S^θ, H^θ, and ZPVE), and one steric descriptor (V_m) were strongly associated with the biotoxicity of amide herbicides. Electrical descriptors showed the greatest impacts on the toxicity of amide herbicides, followed by thermodynamic and steric descriptors.

Effects of three surfactants on the degradation and environmental risk of metolachlor in aquatic environment

Surfactants and pesticides can be simultaneously detected in the environment by the reason of their widespread use and large amounts of emissions. Due to the special amphipathicity of surfactants, it may have special effects on the environmental behaviors and toxic effects of other substances in the environment. There are few relevant studies at present. In this study, the effects of three surfactants on the degradation of the amide pesticide metolachlor in water-sediment system were investigated. The study found that the three surfactants had no significant effect on the degradation of metolachlor in the system at environmental concentrations. However, at critical micelle concentration, cationic surfactant octadecyl trimethyl ammonium bromide and nonionic surfactant nonylphenol polyoxyethylene ether promoted the degradation of metolachlor in water-sediment system. Anionic surfactant odium dodecylbenzene sulfonate (SDBS) prolonged the degradation half-life of metolachlor. The presence of surfactants not only affected the environmental behavior of pesticides. When they coexisted with pesticides, the joint toxicity to aquatic organisms cannot be ignored. This study found that the combined effects of three surfactants and metolachlor on the acute developmental toxicity of zebrafish embryos were all synergistic effects. The combined effects of two ionic surfactants and metolachlor on the acute toxicity of adult zebrafish were synergistic effects. Further study showed that co-exposure of SDBS and metolachlor increased the absorption of metolachlor by zebrafish. Combined exposure of SDBS and metolachlor caused oxidative stress in brain, gill and liver of zebrafish. The results showed that the simultaneous presence of anionic surfactants and pesticides in the environment may increase the environmental risk of pesticides.

Toxicity of Quaternary Ammonium Compounds (QACs) as single compounds and mixtures to aquatic non-target microorganisms: Experimental data and predictive models

The toxic effects of five Quaternary Ammonium Compounds (QACs) that are widely used as active ingredients in personal care products were assessed using the bioluminescent bacterium Aliivibrio fischeri (formerly Vibrio fischeri) (Microtox® test system). The experimental results showed a relevant toxicity for almost all of the single QACs, with IC₅₀ values lower than 1mgL^-1. Analysis of the mode of action through the application of the Quantitative Structure-Activity Relationship (QSAR) models indicated an a-specific reactivity for most of the QACs toward A. fischeri. Only hexadecyl trimethyl ammonium chloride (ATMAC-16) behaved as a polar-narcotic, with a low reactivity toward the bacterial cell membrane. The concentration response curves of the different binary and multicomponent mixtures of QACs were also evaluated with respect to the predictions from the Concentration Addition (CA) and Independent Action (IA) models. For almost all of the binary and multicomponent mixtures (7 out of 11 mixtures tested), an agreement between the experimental and predicted IC_x was observed and confirmed via application of the Model Deviation Ratio (MDR). In four cases, some deviations from the expected behaviour were observed (potential antagonistic and synergistic interactions) at concentrations on the order of hundreds of µgL^-1, which could be of environmental concern, especially in the case of synergistic effects. The analysis of aquatic ecotoxicity data and the few available values of the measured environmental concentrations (MECs) from the literature for wastewaters and receiving waterbodies suggest that a potential risk toward aquatic life cannot be excluded.