Evaluation of combined toxicity of phenols and lead to Photobacterium phosphoreum and quantitative structure-activity relationships

Cascade reaction-based highly sensitive fluorescent sensing systems applicable for dual-pattern fluorescence visualizing of thiophenol flavors in meat products and condiments

Thiophenols (ArSHs) are widely used as popular flavoring ingredients for making daily dishes. Dissecting the ArSHs contents in common foodstuffs is meaningful in the field of food safety science. Herein, a novel small-molecule sensor 2-(1H-benzo[d]imidazol-2-yl)-3-(2-(2,4-dinitrophenoxy)-4-morpholinophenyl)acrylonitrile (NOSA) has been tailored. The NOSA is able to respond to ArSHs, spontaneously yielding highly green-emissive fluorescent iminocoumarin (I₅₀₀). This cascade reaction-based strategy is sensitive (limit-of-detection = 2.8 nM), rapid (within 5 min), and selective toward ArSH flavors. Probe NOSA has been applied to the determination of ArSHs in real-life meat products and condiments. Moreover, a far-red fluorescent compound, 2-(7-(diethylamino)-4-(4-(methylthio)styryl)-2H-chromen-2-ylidene)malononitrile (CMMT), has been first combined with NOSA to construct a composite probe NOSA@CMMT for the ratiometric detection of ArSHs (I₅₀₀/I₆₃₀). System NOSA@CMMT exhibits a conspicuous fluorescence change from deep-red to light-green. Benefitted from the gorgeous chromatic fluctuation, a smartphone-integrated analysis platform is established for the real-time evaluation of ArSHs level.

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.

Predicting cytotoxicity of binary pollutants towards a human cell panel in environmental water by experimentation and deep learning methods

Biological assays are useful in water quality evaluation by providing the overall toxicity of chemical mixtures in environmental waters. However, it is impossible to elucidate the source of toxicity and some lethal combination of pollutants simply using biological assays. As facile and cost-effective methods, computation model-based toxicity assessments are complementary technologies. Herein, we predicted the human health risk of binary pollutant mixtures (i.e., binary combinations of As(III), Cd(II), Cr(VI), Pb(II) and F(I)) in water using in vitro biological assays and deep learning methods. By employing a human cell panel containing human stomach, colon, liver, and kidney cell lines, we assessed the human health risk mimicking cellular responses after oral exposures of environmental water containing pollutants. Based on the experimental cytotoxicity data in pure water, multi-task deep learning was applied to predict cellular response of binary pollutant mixtures in environmental water. Using additive descriptors and single pollutant toxicity data in pure water, the established deep learning model could predict the toxicity of most binary mixtures in environmental water, with coefficient of determination (R²) > 0.65 and root mean squared error (RMSE) < 0.22. Further combining the experimental data on synergistic and antagonistic effects of pollutant mixtures, deep learning helped improve the predictive ability of the model (R² > 0.74 and RMSE <0.17). Moreover, predictive models allowed us identify a number of toxicity source-related physiochemical properties. This study illustrates the combination of experimental findings and deep learning methods in the water quality evaluation.

Quantitative Structure-Toxicity Relationship analysis of combined toxic effects of lignocellulose-derived inhibitors on bioethanol production

This study performed a Quantitative Structure-Toxicity Relationship (QSTR) model to evaluate the combined toxicity of lignocellulose-derived inhibitors on bioethanol production. Compared with all the control groups, the combined systems exhibited lower conductivity values, higher oxidation-reduction potential values, as well as maximum inhibition rates. These results indicated that the presence of combined inhibitors had a negative effect on the bioethanol fermentation process. Meanwhile, QSTR model was excellent for evaluating the combined toxic effects at lower ferulic acid concentration (([1:4] × IC50)) and (([1:1] × IC50)), due to higher R² values (0.994 and 0.762), lower P values (0.000 and 0.023) and relative error values (less than 30%). The obtained results also showed that the combined toxic effects of ferulic acid and representative lignocellulose-derived inhibitors were relevant to different molecular descriptors. Meanwhile, the interactions of combined inhibitors were weaker when ferulic acid was at low concentration ([1:4] × IC50).

Biological enrichment prediction of polychlorinated biphenyls and novel molecular design based on 3D-QSAR/HQSAR associated with molecule docking

Based on the experimental data of octanol-water partition coefficients (Kow, represents bioaccumulation) for 13 polychlorinated biphenyl (PCB) congeners, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to establish 3D-QSAR models, combined with the hologram quantitative structure-activity relationship (HQSAR), the substitution sites (mono-substituted and bis-substituted) and substituent groups (electron-withdrawing hydrophobic groups) that significantly affect the octanol-water partition coefficients values of PCBs were identified, a total of 63 monosubstituted and bis-substituted were identified. Compared with using 3D-QSAR model alone, the coupling of 3D-QSAR and HQSAR models greatly increased the number of newly designed bis-substituted molecules, and the logKow reduction in newly designed bis-substituted molecules was larger than that of monosubstituted molecules. This was established to predict the Kow values of 196 additional PCBs and carry out a modification of target molecular PCB-207 to lower its Kow (biological enrichment) significantly, simultaneously maintaining the flame retardancy and insulativity after calculation by using Gaussian09. Simultaneously, molecular docking could further screen out three more environmental friendly low biological enrichment newly designed PCB-207 molecules (5-methyl-PCB-207, 5-amino-PCB-207, and 4-amino-5-ethyl-PCB-207).

Combined Toxicity of Nitro-Substituted Benzenes and Zinc to Photobacterium Phosphoreum: Evaluation and QSAR Analysis

The single toxicity (IC₅₀) of zinc (Zn) and 11 nitro-substituted benzenes to Photobacterium phosphoreum were determined, respectively. On basis of single toxicity, the joint toxicity of binary mixtures of Zn and 11 nitro-substituted benzenes at different Zn concentrations of 0.2 IC₅₀, 0.5 IC₅₀, and 0.8 IC₅₀ were measured. The joint toxicity was evaluated by toxic unit (TU) and additive index (AI) methods. The results indicated that the joint toxicity was not only depending on the Zn concentrations but also on the substituted groups of nitro-substituted benzenes. The quantitative structure-activity relation (QSAR) equations were developed and the results showed that the toxicity of nitro-substituted benzenes has different joint effect at the different Zn concentrations. At the Zn concentration of 0.2 IC₅₀, the binary joint effects were mainly antagonism and the joint toxicity was negatively related to descriptors called VE2_B(p) and TIC3. At the Zn concentration of 0.5 IC₅₀ and 0.8 IC₅₀, the binary joint effects were mainly antagonism and simple addition, and the joint toxicity was related to the same descriptor Eig06_ AEA(dm). It indicated that the joint toxic actions were similar when combined at the medium and high concentrations of Zn.

Evaluation of joint toxicity of nitroaromatic compounds and copper to Photobacterium phosphoreum and QSAR analysis

The individual toxicities of Cu and 11 nitroaromatic compounds to Photobacterium phosphoreum were determined. The toxicity was expressed as the concentrations causing a 50% inhibition of bioluminescence after 15 min exposure (IC(50)). To evaluate the joint effect between the metal ion and the 11 nitroaromatic compounds, the joint toxicity of Cu and 11 nitroaromatic compounds were measured at different Cu concentrations (0.2IC(50), 0.5IC(50) and 0.8IC(50)), respectively. The result shows that the binary joint effect between Cu and nitroaromatic compounds is mainly simple addition at the low Cu concentration (0.2IC(50)). However, an antagonism effect, 55% and 64%, was observed between Cu and 11 nitroaromatic compounds for Cu at medium and high concentrations (0.5IC(50) and 0.8IC(50)). Quantitative structure-activity relationship (QSAR) analysis was performed to study the joint toxicity for the 11 nitroaromatic compounds. The result shows that the toxicity of nitroaromatic compounds is related to descriptors of Connolly solvent-excluded volume (CSEV) and dipolarity/polarizability (S) at low Cu concentration. On the other hand, the toxicity is related to Connolly accessible area (CAA) at medium and high Cu concentrations. The result indicates that different QSAR models on complex mixtures need to be developed to assess the ecological risk in real environments. Using single toxic data to evaluate the toxic effect of mixtures may result in wrong conclusions.