Food Safety: Chemical Agents

Discrimination of the acute toxicity of chemicals to guppy (Poecilia reticulata), fathead minnow (Pimephales promelas) and rainbow trout (Oncorhynchus mykiss) with nonpolar narcosis and other Modes Of Action (MOA) was successfully carried out by means of discrimination analysis and structural similarity analysis. Good QSARs for acute aquatic toxicity of chemicals with nonpolar narcosis MOA for the three fish species were constructed, based on molecular polarizability and H‐bond acceptor factors calculated by the HYBOT program. These latter relationships are useful alternatives to QSARs based on the experimental parameter log Ko/w because they yield good statistics and because they are amenable to physicochemical interpretation. In this case, the acute aquatic toxicity of chemicals with nonpolar MOA is presented as the result of two contributions (molecular polarizability, which is volume‐related, and hydrogen bonding) with opposite action: molecular polarizability increases acute nonpolar narcosis toxicity and H‐bond acceptor ability decreases it.

Acute episodes of massive air pollution with harmful effects are fortunately rare, but this means that no one is very experienced at investigating such risks. The immediate effects may be...

Currently, there is widespread interest in exploiting "omics" approaches to screen the toxicity of chemicals, potentially enabling their rapid categorization into classes of defined mode of action (MOA). Direct infusion Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) metabolomics provides a sensitive and nontargeted analysis of potentially a thousand endogenous metabolites. Our previous work has shown that mass spectra can be recorded from whole-organism homogenate or hemolymph of single adult Daphnia magna. Here we develop multivariate models and discover perturbations to specific metabolic pathways that can discriminate between the acute toxicities of four chemicals to D. magna using FT-ICR MS metabolomics. We focus on model toxicants (cadmium, fenvalerate, dinitrophenol, and propranolol) with different MOAs. First, we confirmed that a toxicant-induced metabolic effect could be determined for each chemical in both the hemolymph and the whole-organism metabolome, with between 9 and 660 mass spectral peaks changing intensities significantly, dependent upon toxicant and sample type. Subsequently, supervised multivariate models were built that discriminated significantly all four acute metabolic toxicities, yielding mean classification error rates (across all classes) of 3.9 and 6.9% for whole-organism homogenates and hemolymph, respectively. Following extensive peak annotation, we discovered toxicant-specific perturbations to putatively identified metabolic pathways, including propranolol-induced disruption of fatty acid metabolism and eicosanoid biosynthesis and fenvalerate-induced disruption of amino sugar metabolism. We conclude that the metabolic profiles of whole-daphnid homogenates are more discriminatory for toxicant action than hemolymph. Furthermore, our findings highlight the capability of metabolomics to discover early-event metabolic responses that can discriminate between the acute toxicities of chemicals.

BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees

Introduction. Considerable attention is paid to the assessment of acute toxicity of chemical compounds during oral administration due to the different rates of absorption of substances in different animal species and various experimental conditions. Given the pace of development of the chemical industry, researchers are faced with the question of accelerating the study of the properties of substances and filling data gaps. Therefore, quantitative prediction of the toxic properties of substances using mathematical models based on the structure or structural properties of compounds - quantitative structure -activity relationship (QSAR) modeling - is one of the promising areas. The purpose of this study is to create and compare the performance of the obtained mathematical models for predicting the acute toxicity of various classes of chemicals. Materials and methods. The study included four classes of pesticides (organochlorine compounds (OCs), azoles, carbamates, organophosphorus compounds (OPs) in the amount of 100 compounds with descriptors calculated by PaDEL-Descriptors software ver. 2.21. Regression models were constructed in the WEKA software, subjected to an internal validation procedure. Statistical parameters such as the mean square error (RMSE) and the coefficient of determination (r 2) were used to assess the quality of regression models. Results. To predict acute oral toxicity of OCs and OPs, it is optimal to use a model in which neural networks and the support vector method are combined, for carbamates - an ensemble model that includes linear regression and the support vector method. For substances from the azole group, it was not possible to create a model that would meet the necessary requirements: r 2>0.6 for the training set and r 2 >0.5 for cross-validation. Limitations. The study is limited by the number of compounds studied, the class of chemical compounds, and the area of distribution of the results obtained during modeling. Conclusion. In this study, ensemble modelling methods demonstrated the best results in predicting acute oral toxicity for OCs, carbamates, and OPs.

The relationship of the acute LC50 of chemicals to their chronic toxicity for aquatic animals can be expressed as the acute chronic ratio (ACR). Based on ACR data accumulated from the literature, the size ranges of ACRs for various chemicals were determined for different species. The relationship and significance of the size of the ACR for various species, LC50 values, bioconcentration factors, classes of chemicals and their uses and mode of toxic action are given. Eighty-six percent of the LC50 acute toxicity data were less than two orders of magnitude different from the chronic toxicity no-effect concentration for the same chemicals and species. Among the industrial organic chemicals (i.e., excluding pesticides and metals) the average ACR for four species of organisms was 12. Ninety-three percent of these ACR values were 25 or below. Industrial organic chemicals have a higher percentage of ACR values below 25 do than pesticides and heavy metals. These data offer a statistical basis for the prediction of chronic toxicity from acute toxicity.

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC50) in marine crustaceans using a set of calculated 2D molecular descriptors. The models were built following the guidelines recommended by the OECD (Organization for Economic Cooperation and Development) and validated through stringent processes (internal and external validation). Six machine learning (ML) models were built and compared (linear regression: LR; support vector machine: SVM; random forest: RF; feed-forward backpropagation-based artificial neural network: ANN; decision trees: DT and naïve Bayes: NB) for regression and classification analysis to predict toxicities. All the models displayed encouraging results with high generalisability: the feed-forward-based backpropagation method showed the best results with determination coefficient R2 values of 0.82 and 0.94, respectively, for training set (TS) and validation set (VS). For classification-based modelling, the DT model performed the best with an accuracy (ACC) of 100 % and an area under curve (AUC) value of 1 for both TS and VS. These models showed the potential to replace animal testing for the chemical hazard assessment of untested biocides if they fall within the applicability domain of the proposed models. In general, the models are highly interpretable and robust, with good predictive performance. The models also displayed a trend indicating that toxicity is largely influenced by factors such as lipophilicity, branching, non-polar bonding and saturation of molecules.

The influence of the dilution medium quality on the ecotoxicological response of test organisms using microbiotests was studied. The toxicity of reference toxicants (potassium dichromate, zinc sulfate, phenol) and of a landfill leachate and a process water discharged from a metal finishing plant was assessed in various dilution media including standard test media, 3 mineral waters and 28 river waters. The tests used were the Rotoxkit™ with the freshwater rotifer Brachionus calyciflorus, the Thamnotoxkit™ with the crustacean Thamnocephalus platyurus, and the Lumistox® with Vibrio flscheri. The river and mineral waters generally induced a lower toxicity of reference toxicants compared to the standard media. Hardness associated with Mg and the Ca/Mg ratio might explain in most cases the attenuation of toxicity by natural dilution media (i.e. river waters). With the landfill leachate and the process water, the effects of dilution waters on the acute toxicity were significant but not consistent, i.e. increase or decrease of toxicity was observed for the same sample, depending on the type of dilution water used.

Estrogenicity and acute toxicity of selected anilines using a recombinant yeast assay

REACH regulation demands information about acute toxicity of chemicals towards fish and supports the use of QSAR models, provided compliance with OECD principles. Existing models present some drawbacks that may limit their regulatory application. In this study, a dataset of 908 chemicals was used to develop a QSAR model to predict the LC50 96 hours for the fathead minnow. Genetic algorithms combined with k nearest neighbour method were applied on the training set (726 chemicals) and resulted in a model based on six molecular descriptors. An automated assessment of the applicability domain (AD) was carried out by comparing the average distance of each molecule from the nearest neighbours with a fixed threshold. The model had good and balanced performance in internal and external validation (182 test molecules), at the expense of a percentage of molecules outside the AD. Principal Component Analysis showed apparent correlations between model descriptors and toxicity.

Suitability of the trans/cis Ratio of Unsaturated Fatty Acids in Pseudomonas putida NCTC 10936 as an Indicator of the Acute Toxicity of Chemicals

HASEGAWA, A. T., AND WANG, RICHARD I. H. Attentuation of Increased Vascular Permeability and Toxicity of a Radioprotective Chemical Mixture by Chlorpromazine in Mice. Radiat. Res. 45, 364-372, (1971). The toxicity of a chemical mixture containing 5-hydroxytryptamine (5-HT), 2-aminoethylisothiouronium (AET), and 2-mercaptoethylamine (MEA) was studied in normal and irradiated mice. Toxicity was manifested by severe motor depression, respiratory difficulty, cyanosis, and death. Mortality from acute chemical toxicity was more prevalent in irradiated mice than in normal mice. An increase in the diffusion of Evans blue and a concomitant increase in hematocrit were observed after administration of either 5-HT or the mixture. Neither AET nor MEA produced any significant changes. Pretreatment with chlorpromazine (CPZ) reduced the magnitude of the rise in hematocrit which followed the administration of the mixture and prevented death from chemical toxicity during multiple radiation exposures. CPZ also reduced slightly the radio

We investigated the connection between the data preprocessing strategy and kernel choice on the quality of the associated basic probabilistic neural network models for the acute toxicity of various chemicals to the fathead minnow and to Vibrio fischeri bacteria. The models employ exclusively structural parameters and physicochemical properties as inputs. Results show that the Gaussian kernel is preferable over the reciprocal kernel model. Data preprocessing based on the hyperbolic tangent and the sigmoid logistic transforms provides the best results at the level of the cross validation experiment. Improved models based on cross validation partial models and linear corrections were also investigated. The results show that the improved models with data preprocessing based on the hyperbolic tangent and the finite interval transforms are the best with practically identical quality of predictions.

Synthetic antioxidants: Biochemical actions and interference with radiation, toxic compounds, chemical mutagens and chemical carcinogens

Quantitative comparisons of acute toxicity of organic chemicals to rat and fish