Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors.

Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure-activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were Ntrain = 62, R2 = 0.727, RMS = 0.494, F = 159.537, Q2LOO = 0.727, and Q2pred = 0.725 for the training set; and Ntest = 17, R2 = 0.721, RMS = 0.508, F = 38.773, and q2ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: Ntrain = 62, R2 = 0.956, RMS = 0.199, F = 1279.919, Q2LOO = 0.955, and Q2pred = 0.855 for the training set; and Ntest = 17, R2 = 0.880, RMS = 0.367, F = 110.980, and q2ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.

Preparation of gold nanoparticles supported on graphene oxide with flagella as the template for nonenzymatic hydrogen peroxide sensing.

Gold nanoparticles supported on graphene oxide with flagella as the template were developed as an electrochemical sensor for the detection of hydrogen peroxide (H2O2) in serum. The flagella-Au nanoparticles composite and graphene oxide were dropped onto a glassy carbon electrode (GCE) to form a new H2O2 electrochemical sensor. The structure morphology of the prepared sensor was characterized by transmission electron microscopy (TEM), and the electrocatalytic performance towards H2O2 reduction was evaluated by cyclic voltammetry (CV) and amperometric methods. The response current of the sensor showed a good linear relationship with the concentration of H2O2 in the range of 10-1000 µM (R2 = 0.9916). The minimum detection limit of 1 µM was obtained (S/N = 3). Finally, the sensor was applied to the detection of H2O2 in serum, and the recoveries were satisfactory. As the sensor is sensitive, fast, and easy to make, it is expected to be used for rapid detection of H2O2. Graphical abstract ᅟ.

In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

Dispersive liquid-liquid microextraction combined with non-aqueous capillary electrophoresis for the determination of imazalil, prochloraz and thiabendazole in apples, cherry tomatoes and grape juice.

BACKGROUND: Fruit and vegetables are frequently treated with fungicides to reduce possible spoilage. As a result, fungicide residues may be accumulated in derived products. This important group of chemical compounds has been heavily regulated because of their potential toxicity. Therefore, a simple and rapid method to determine fungicides is desired. RESULTS: A simple non-aqueous capillary electrophoresis (NACE) method based on dispersive liquid-liquid microextraction (DLLME) has been proposed for the determination of imazalil, prochloraz and thiabendazole fungicides in fruits and juice samples. Separation buffer consisted of a methanol-acetonitrile mixture (35:65, v/v) containing 30 mmol L⁻¹ ammonium chloride and 0.5% phosphoric acid. The optimum DLLME conditions were 80 µL trichloromethane as extraction solvent, 0.5 mL tetrahydrofuran as disperser solvent, sample solution pH at 6.0, 5% (w/v) NaCl and 10 s extraction time. Recoveries obtained for various samples ranged from 72% to 102%, with relative standard deviation lower than 6.4%. The limits of detection ranged from 0.47 to 0.72 µg kg⁻¹. CONCLUSION: The proposed method takes the advantages of DLLME and NACE. It is rapid, accurate, sensitive and reproducible for the determination of imazalil, prochloraz and thiabendazole in fruit samples.

Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions.

Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure-activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP-NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.

Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes.

The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.

Determination of neotame in non-alcoholic beverage by capillary zone electrophoresis.

BACKGROUND: Neotame (NEO) is a new artificial sweetener approved as a food additive in many countries. The present method for the determination of NEO in various foodstuffs is high-performance liquid chromatography (HPLC). There are no reports on the determination of NEO in foods by capillary zone electrophoresis (CZE). Therefore a simple and rapid method to determine NEO is desired for quality control. RESULTS: A CZE method combined with solid phase extraction was developed for the determination of NEO in non-alcoholic beverage. The optimum separation conditions obtained were 20 mmol L(-1) sodium borate buffer, pH 8, 25 kV applied voltage, 5 s hydrodynamic injection at 30 mbar and ultraviolet detection at 191 nm. The calibration curve showed good linearity (R(2) = 1.000) in the range 0.5-100 µg mL(-1) , and the limit of detection was 0.118 µg mL(-1) . The method was successfully applied to the determination of NEO in two kinds of beverage with migration time less than 5 min, relative standard deviation (n = 3) less than 2% and recoveries ranging from 90 to 95%. CONCLUSION: The proposed CZE method has the advantages of shorter analysis time and lower cost compared with HPLC, indicating that it may be a good alternative to the HPLC method.

Determination of melamine in milk powder, milk and fish feed by capillary electrophoresis: a good alternative to HPLC.

BACKGROUND: Since September 2008, an increased incidence of kidney stones and renal failure in infants, associated with the ingestion of infant formula contaminated with melamine has been reported in China. Furthermore, melamine was not only found in many protein-based food commodities, but also in the feeds for cattle and poultry. So it is necessary to develop a suitable method to determine melamine. RESULTS: A capillary zone electrophoresis (CZE) method for analysis of melamine was developed by use of running electrolyte containing 35 mmol L(-1) sodium dihydrogen phosphate at pH 3.5, with UV detection at 210 nm. Regression equation revealed linear relationships (r = 0.9999) between the peak-area and the content of melamine from 0.8 to 80 µg mL(-1). The detection limit was 0.08 µg mL(-1). The method was successfully applied to the determination of melamine in milk powder, milk and fish feed, with the recoveries from 94.5% to 103.7%. CONCLUSION: The performance of the CZE method evaluated in terms of precision, limits of detection, accuracy and quantification were comparable and in good agreement with those obtained by the HPLC method, with the advantage of shorter analysis time and lower cost.