Prediction Of Chemical Toxicity Research Articles

Profiles of Chemical Effects on Cells (pCEC): a toxicogenomics database with a toxicoinformatics system for risk evaluation and toxicity prediction of environmental chemicals

Profiles of Chemical Effects on Cells (pCEC) is a toxicogenomics database with a system of classifying chemicals that have effects on human health. This database stores and handles gene expression profiling information and categories of toxicity data. Chemicals are classified according to the specific tissues and cells they affect, the gene expression changes they induce, their toxicity and biological functions in this database system. The pCEC system also analyzes relationships between chemicals and the genes they affect in specific tissues and cells. The reason why we developed pCEC is to support decision-making within the context of environmental regulation. Especially, exposure to environmental chemicals during fetal and newborn development may result in a predisposition to various disorders such as cancer, learning disabilities and allergies later in life. The identification and prediction of hazardous chemicals using limited information are important issues in human health risk management. Therefore, various toxicity information including lethal dose 50 (LD50), toxicity pathways and pathological data were loaded into pCEC. pCEC is also a facility for query, analysis and prediction of unknown toxicochemical reaction pathways and biomarkers which are based on toxicoinformatical data mining approaches. This database is available online at http://project.nies.go.jp/eCA/cgi-bin/index.cgi. The current version of the database has information on the hepatotoxicity, reproductive toxicity and embryotoxicity of chemicals.

Toxicity evaluation and prediction of toxic chemicals on activated sludge system

The gaps of data for evaluating toxicity of new or overloaded organic chemicals on activated sludge system resulted in the requirements for methodology of toxicity estimation. In this study, 24 aromatic chemicals typically existed in the industrial wastewater were selected and classified into three groups of benzenes, phenols and anilines. Their toxicity on activated sludge was then investigated. Two indexes of IC 50-M and IC 50-S were determined respectively from the respiration rates of activated sludge with different toxicant concentration at mid-term (24 h) and short-term (30 min) time intervals. Experimental results showed that the group of benzenes was the most toxic, followed by the groups of phenols and anilines. The values of IC 50-M of the tested chemicals were higher than those of IC 50-S. In addition, quantitative structure–activity relationships (QSARs) models developed from IC 50-M were more stable and accurate than those of IC 50-S. The multiple linear models based on molecular descriptors and K ow presented better reliability than single linear models based on K ow. Among these molecular descriptors, E lumo was the most important impact factor for evaluation of mid-term toxicity.

Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species

Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chemical toxicology data.

An in vitro model for the prediction of chemical metabolism and toxicity

•In order to circumvent these issues, we have generated a cell line that reports on a specific pathway of oxidative stress. The antioxidant response element (ARE) is a transcriptional regulatory element involved in the activation of cytoprotective genes coding for a number of antioxidant proteins and detoxifying enzymes. This cell line measures Nrf2 mediated activation of the ARE via a luciferase reporter.

Classification and Quantification of the Toxicity of Chemicals to Guppy, Fathead Minnow, and Rainbow Trout. Part 2. Polar Narcosis Mode of Action

AbstractThis paper is devoted to the classification of chemicals with polar narcosis Mode of Action (MOA) to guppy, fathead minnow and rainbow trout, and to creation of QSARs for toxicity prediction of such chemicals on the basis of physicochemical descriptors calculated by the HYBOT program. More than 85% of all chemicals were correctly classified (polar narcosis/other MOA, polar narcosis/baseline toxicity (nonpolar narcosis), polar narcosis+baseline toxicity/other MOA) on the basis of structural similarity. Regression equations with good statistical criteria using HYBOT physicochemical descriptors were obtained for the toxicity of chemicals with polar narcosis MOA. Comparison of those equations with the equations for baseline toxicity permitted estimation of the essential contribution of baseline toxicity to the toxicity of all chemicals with polar narcosis MOA. The ratio of polar and nonpolar contributions depends on hydrophobity. Such behavior can be related to the increased opportunity of a hydrophobic chemical to move in all directions in the hydrocarbon‐like interior of the membrane.

Reproducible chemical-induced changes in gene expression profiles in human hepatoma HepaRG cells under various experimental conditions

The use of in vitro human liver cell models is an attractive approach in toxicogenomic studies designed to analyze gene expression changes induced by a toxic chemical. However, in such studies, reliability, reproducibility and interlaboratory concordance of microarrays, as well as the choice of the most suitable cell model, remain a matter of debate. This work was aimed at evaluating the robustness of microarray technologies and the suitability of the highly differentiated human HepaRG cell line in the investigation of gene expression changes induced by a toxic compound in human liver. The influence of various experimental conditions including cell cultures grown at different test sites, different generations of microarrays, RNA analysis platforms and softwares, was tested on gene expression profiles induced by a 20 h treatment with an 8 mM concentration of phenobarbital as the toxic compound. As many as 1099 genes ( p-value < 0.01 and 1.5-fold-change), representing 74% and 30% of the signature genes detected with Agilent 22 and 44K pangenomic microarrays, respectively, were shown to be modulated in common in six independently performed experiments. The most modulated genes included both those known to be regulated by phenobarbital, such as cytochromes P450 and membrane transporters, and those involved in oxidative stress, inflammation and apoptosis, typifying a toxic insult. These data provide strong support for the use of a toxicogenomic approach for the in vitro prediction of chemical toxicity, and for the choice of human HepaRG cells as a promising model system for human hepatotoxicity testing.

Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure–activity relationships methods

This paper presents the results of an optimization study on the toxicity of 91 aliphatic and aromatic compounds as well as a small subset of pesticides to algae Chlorella vulgaris, which was accomplished by using quantitative structure–activity relationships (QSAR). The linear (HM) and the nonlinear method radial basis function neural networks (RBFNN) were used to develop the QSAR models and both of them can give satisfactory prediction results. At the same time, by interpreting the descriptors, we can get some insight into structural features (molecular surface area, electrostatic repulsion, and hydrogen bonds) related to the toxic action. Finally, a detailed analysis on the model application domain defined the compounds, whose estimation can be accepted with confidence. The results of this study suggest that the proposed approaches could be successfully used as a general tool for the estimate of novel toxic compounds.

Prediction of chemical toxicity with local support vector regression and activity-specific kernels

We propose a new kernel, based on 2-D structural chemical similarity, that integrates activity-specific information from the training data, and a new approach to applicability domain estimation that takes feature significances and activity distributions into consideration. The new kernel provides superior results than the well-established Tanimoto kernel, and activity-sensitive feature selection enhances prediction quality. Validation of local support vector regression models based on this kernel has been preformed with three publicly available datasets from the DSSTox project. One of them (Fathead Minnow Acute Toxicity) has been already modelled by other groups, and serves as a benchmark dataset, the other two (Maximum Recommended Therapeutic Dose, IRIS Lifetime Cancer Risk) have been modelled for the first time according to the knowledge of the authors. For all three models predictive accuracies increase with the prediction confidences that indicate the applicability domain. Depending on the confidence cutoff for acceptable predictions we were able to achieve > 90% predictions within 1 log unit of the experimental data for all datasets.

Studies with neuronal cells: From basic studies of mechanisms of neurotoxicity to the prediction of chemical toxicity

Neurotoxicology considers that chemicals perturb neurological functions by interfering with the structure or function of neural pathways, circuits and systems. Using in vitro methods for neurotoxicity studies should include evaluation of specific targets for the functionalism of the nervous system and general cellular targets. In this review we present the neuronal characteristics of primary cultures of cortical neurons and of cerebellar granule cells and their use in neurotoxicity studies. Primary cultures of cortical neurons are constituted by around 40% of GABAergic neurons, whereas primary cultures of cerebellar granule cells are mainly constituted by glutamatergic neurons. Both cultures express functional GABAA and ionotropic glutamate receptors. We present neurotoxicity studies performed in these cell cultures, where specific neural targets related to GABA and glutamate neurotransmission are evaluated. The effects of convulsant polychlorocycloalkane pesticides on the GABAA, glycine and NMDA receptors points to the GABAA receptor as the neural target that accounts for their in vivo acute toxicity, whereas NMDA disturbance might be relevant for long-term toxicity. Several compounds from a list of reference compounds, whose severe human poisoning result in convulsions, inhibited the GABAA receptor. We also present cell proteomic studies showing that the neurotoxic contaminant methylmercury affect mitochondrial proteins. We conclude that the in vitro assays that have been developed can be useful for their inclusion in an in vitro test battery to predict human toxicity.

ECOSAR model performance with a large test set of industrial chemicals

The widely used ECOSAR computer programme for QSAR prediction of chemical toxicity towards aquatic organisms was evaluated by using large data sets of industrial chemicals with varying molecular structures. Experimentally derived toxicity data covering acute effects on fish, Daphnia and green algae growth inhibition of in total more than 1000 randomly selected substances were compared to the prediction results of the ECOSAR programme in order (1) to assess the capability of ECOSAR to correctly classify the chemicals into defined classes of aquatic toxicity according to rules of EU regulation and (2) to determine the number of correct predictions within tolerance factors from 2 to 1000. Regarding ecotoxicity classification, 65% (fish), 52% (Daphnia) and 49% (algae) of the substances were correctly predicted into the classes “not harmful”, “harmful”, “toxic” and “very toxic”. At all trophic levels about 20% of the chemicals were underestimated in their toxicity. The class of “not harmful” substances (experimental LC/EC50>100mgl−1) represents nearly half of the whole data set. The percentages for correct predictions of toxic effects on fish, Daphnia and algae growth inhibition were 69%, 64% and 60%, respectively, when a tolerance factor of 10 was allowed. Focussing on those experimental results which were verified by analytically measured concentrations, the predictability for Daphnia and algae toxicity was improved by approximately three percentage points, whereas for fish no improvement was determined. The calculated correlation coefficients demonstrated poor correlation when the complete data set was taken, but showed good results for some of the ECOSAR chemical classes. The results are discussed in the context of literature data on the performance of ECOSAR and other QSAR models.

Biochemical applications of ultrathin films of enzymes, polyions and DNA.

This feature article summarizes recent applications of ultrathin films of enzymes and DNA assembled layer-by-layer (LbL). Using examples mainly from our own research, we focus on systems developed for biocatalysis and biosensors for toxicity screening. Enzyme-poly(L-lysine) (PLL) films, especially when stabilized by crosslinking, can be used for biocatalysis at unprecedented high temperatures or in acidic or basic solutions on electrodes or sub-micron sized beads. Such films have bright prospects for chiral synthesis and biofuel cells. Excellent bioactivity and retention of enzyme structure in these films facilitates their use in detailed kinetic studies. Biosensors and arrays employing DNA-enzyme films show great promise in predicting genotoxicity of new drug and chemical product candidates. These devices combine metabolic biocatalysis, reactive metabolite-DNA reactions, and DNA damage detection. Catalytic voltammetry or electrochemiluminescence (ECL) can be used for high throughput arrays utilizing multiple LbL "spots" of DNA, enzyme and metallopolymer. DNA-enzyme films can also be used to produce nucleobase adduct toxicity biomarkers for detection by LC-MS. These approaches provide valuable high throughput tools for drug and chemical product development and toxicity prediction.

A review on advantages of implementing luminescence inhibition test (Vibrio fischeri) for acute toxicity prediction of chemicals

Evaluation of biological effects using a rapid, sensitive and cost effective method can indicate specific information on toxicity/ecotoxicity. Since assays based on animals, plants and algae are expensive, time consuming and require large sample volume, recent studies have emphasized the benefits of rapid, reproducible and cost effective bacterial assays for toxicity screening and assessment. This review focuses on a bacterial assay, i.e., Vibrio fischeri bioluminescence inhibition assay, which is often chosen as the first test in a test battery based on speed and cost consideration. The test protocol is simple and was originally applied for aqueous phase samples or extracts. The versatility of the assay has increased with subsequent modification, i.e., the kinetic assay for turbid and colored samples and the solid phase test for analyzing sediment toxicity. Researchers have reported the Vibrio fischeri bioluminescence assay as the most sensitive across a wide range of chemicals compared to other bacterial assays such as nitrification inhibition, respirometry, ATP luminescence and enzyme inhibition. This assay shows good correlations with other standard acute toxicity assays and is reported to detect toxicity across a wide spectrum of toxicants.

The practice of structure activity relationships (SAR) in toxicology.

Both qualitative and quantitative modeling methods relating chemical structure to biological activity, called structure-activity relationship analyses or SAR, are applied to the prediction and characterization of chemical toxicity. This minireview will discuss some generic issues and modeling approaches that are tailored to problems in toxicology. Different approaches to, and some facets and limitations of the practice and science of, SAR as they pertain to current toxicology analyses, and the basic elements of SAR and SAR-model development and prediction systems are discussed. Other topics include application of 3-D SAR to understanding of the propensity of chemicals to cause endocrine disruption, and the use of models to analyze biological activity of metal ions in toxicology. An example of integration of knowledge pertaining to mechanisms into an expert system for prediction of skin sensitization to chemicals is also discussed. This minireview will consider the utility of modeling approaches as one component for better integration of physicochemical and biological properties into risk assessment, and also consider the potential for both environmental and human health effects of chemicals and their interactions.

Application of artificial intelligence and computer-based methods to predicting chemical toxicity

The toxicity prediction problem lies squarely at the interface of biology, chemistry and computational domains and will require the integrated application of knowledge and approaches from each domain for its solution. It is not a single modeling problem, but rather multiple levels of modeling compartments derived from the goals of toxicity risk assessment. These compartments include different categories and characteristics of toxicity (e.g., cancer vs. non-cancer, acute vs. delayed) and, therein, different levels of biofunctional organization and multiple mechanisms of toxicity extending to the level of individual chemical structures. A toxicity prediction model should strive to resolve the global toxicity prediction problem to local modeling compartments that reflect coherent biofunctional mechanisms and common modes of action while retaining some level of useful generalizations. This paper attempts to use these general concepts to frame the challenges and opportunities for application of Artificial Intelligence (AI) and computer-based methods to the goal of chemical toxicity prediction.

Toxicity prediction of chemicals based on structure-activity relationships

The prediction system of chemical toxicity has been developed by means of structure-activity relationships (SARs) based on the computerized fact database (BL-DB). Number and ratio of elements, side chains, bonding, position and microenvironment of side chains were used as the structural factors of chemicals for toxicity prediction. Such information was obtained from BL-DB by Wiswesser line notation. In the present study, the Salmonella-microsome assay of the mutagenicity was chosen as indicative of the target toxicity of chemicals. A set of chemicals specified with mutagenicity data was retrieved, and the necessary information was extracted and transferred to the working file. Rules of the relation between characteristics of chemical structure and the assay results are extracted as parameters for rules by experts on rearranged data set. There are analysed statistically by discriminant analysis and prediction and the rules were evaluated by the elimination method. For example, eight kinds of rules to predict Salmonella/microsomal assay were constructed, and currently results of the assay on organic compounds can be predicted above 90% accuracy.

W13/L5 - Toxicity prediction of chemicals based on structure-activity relationships

W13/L5 - Toxicity prediction of chemicals based on structure-activity relationships

Quantitative Structure-Based Modeling Applied to Characterization and Prediction of Chemical Toxicity

Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure–activity relationships (QSARs). In addition to modeling of chemical toxicity, these methods have been extensively applied to modeling of medicinal properties of chemicals. However, there are important differences in the nature and objectives of these two applications, which have led to the evolution of different modeling approaches (namely, the need for treating sets of noncongeneric toxic compounds). In this paper are discussed those approaches to chemical toxicity that have taken a more “personalized” configuration and have undergone implementation into software programs able to perform the various steps of the assessment of the hazard posed by the chemicals. These models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism.

The expert system for toxicity prediction of chemicals based on structure-activity relationship.

The prediction systems of chemical toxicity has been developed by means of structure-activity relationship based on the computerized fact database (BL-DB). Numbers and ratio of elements, side chains, bonding, position, and microenvironment of side chains were used as structural factors of the chemical for the prediction. Such information was obtained from the BL-DB database by Wiswesser line-formula chemical notation. In the present study, the Salmonella/microsome assay was chosen as indicative of the target toxicity of chemicals. A set of chemicals specified with mutagenicity data was retrieved, and necessary information was extracted and transferred to the working file. Rules of the relations between characteristics of chemical structure and the assay result are extracted as parameters for rules by experts on the rearranged data set. These were analyzed statistically by the discriminant analysis and the prediction with the rules were evaluated by the elimination method. Eight kinds of rules to predict Salmonella/microsome assay were constructed, and currently results of the assay on aliphatic and heterocyclic compounds can be predicted as accurately as +90%.

Use of submitochondrial particles for prediction of chemical toxicity in man

The mitochondrial tests are good indicators of the relative toxicity of many xenobiotics to cells and fish. The current investigation studies the ability of these procedures to predict in vivo tissue concentrations associated with clinical illness in man