Computational Toxicology Methods Research Articles

Persulfate-Based Advanced Oxidation Process for Chlorpyrifos Degradation: Mechanism, Kinetics, and Toxicity Assessment.

Persulfate-based advanced oxidation process has been proven to be a promising method for the toxic pesticide chlorpyrifos (CPY) degradation in wastewater treatment. However, due to the limitation for the short-lived intermediates detection, a comprehensive understanding for the degradation pathway remains unclear. To address this issue, density functional theory was used to analyze the degradation mechanism of CPY at the M06-2X/6-311++G(3df,3pd)//M06-2X/6-31+G(d,p) level, and computational toxicology methods were employed to explore the toxicity of CPY and its degradation products. Results show that hydroxyl radicals (·OH) and sulfate radicals (SO4•-) initiate the degradation reactions by adding to the P=S bond and abstracting the H atom on the ethyl group, rather than undergoing α-elimination of the pyridine ring in the persulfate oxidation process. Moreover, the addition products were attracted and degraded by breaking the P-O bond, while the abstraction products were degraded through dealkylation reactions. The transformation products, including 3,5,6-trichloro-2-pyridynol, O,O-diethyl phosphorothioate, chlorpyrifos oxon, and acetaldehyde, obtained through theoretical calculations have been detected in previous experimental studies. The reaction rate constants of CPY with ·OH and SO4•- were 6.32 × 108 and 9.14 × 108 M-1·s-1 at room temperature, respectively, which was consistent with the experimental values of 4.42 × 109 and 4.5 × 109 M-1 s-1. Toxicity evaluation results indicated that the acute and chronic toxicity to aquatic organisms gradually decreased during the degradation process. However, some products still possess toxic or highly toxic levels, which may pose risks to human health. These research findings contribute to understanding the transformation behavior and risk assessment of CPY in practical wastewater treatment.

Dissecting the Enantioselective Neurotoxicity of Isocarbophos: Chiral Insight from Cellular, Molecular, and Computational Investigations.

Chiral organophosphorus pollutants are found abundantly in the environment, but the neurotoxicity risks of these asymmetric chemicals to human health have not been fully assessed. Using cellular, molecular, and computational toxicology methods, this story is to explore the static and dynamic toxic actions and its stereoselective differences of chiral isocarbophos toward SH-SY5Y nerve cells mediated by acetylcholinesterase (AChE) and further dissect the microscopic basis of enantioselective neurotoxicity. Cell-based assays indicate that chiral isocarbophos exhibits strong enantioselectivity in the inhibition of the survival rates of SH-SY5Y cells and the intracellular AChE activity, and the cytotoxicity of (S)-isocarbophos is significantly greater than that of (R)-isocarbophos. The inhibitory effects of isocarbophos enantiomers on the intracellular AChE activity are dose-dependent, and the half-maximal inhibitory concentrations (IC50) of (R)-/(S)-isocarbophos are 6.179/1.753 μM, respectively. Molecular experiments explain the results of cellular assays, namely, the stereoselective toxic actions of isocarbophos enantiomers on SH-SY5Y cells are stemmed from the differences in bioaffinities between isocarbophos enantiomers and neuronal AChE. In the meantime, the modes of neurotoxic actions display that the key amino acid residues formed strong noncovalent interactions are obviously different, which are related closely to the molecular structural rigidity of chiral isocarbophos and the conformational dynamics and flexibility of the substrate binding domain in neuronal AChE. Still, we observed that the stable "sandwich-type π-π stacking" fashioned between isocarbophos enantiomers and aromatic Trp-86 and Tyr-337 residues is crucial, which notably reduces the van der Waals' contribution (ΔGvdW) in the AChE-(S)-isocarbophos complexes and induces the disparities in free energies during the enantioselective neurotoxic conjugations and thus elucidating that (S)-isocarbophos mediated by synaptic AChE has a strong toxic effect on SH-SY5Y neuronal cells. Clearly, this effort can provide experimental insights for evaluating the neurotoxicity risks of human exposure to chiral organophosphates from macroscopic to microscopic levels.

In Vitro and Predictive Computational Toxicology Methods for the Neurotoxic Pesticide Amitraz and Its Metabolites

The Varroa destructor parasite is responsible for varroasis in honeybees worldwide, the most destructive disease among parasitic diseases. Thus, different insecticides/acaricides have been widely used within beehives to control these parasitic diseases. Namely, amitraz is the most used acaricide due to its high efficacy shown against Varroa destructor. However, pesticides used for beehive treatments could be incorporated into the honey and accumulate in other hive products. Hence, honeybee health and the impairment of the quality of honey caused by pesticides have gained more attention. Amitraz and its main metabolites, N-(2,4-dimethylphenyl) formamide (2,4-DMF) and 2,4-dimethylaniline (2,4-DMA), are known to be potent neurotoxicants. In this research, the cytotoxicity of amitraz and its metabolites has been assessed by MTT and PC assays in HepG2 cells. In addition, possible target receptors by in silico strategies have been surveyed. Results showed that amitraz was more cytotoxic than its metabolites. According to the in silico ADMEt assays, amitraz and its metabolites were predicted to be compounds that are able to pass the blood-brain barrier (BBB) and induce toxicity in the central and peripheral nervous systems. The main target class predicted for amitraz was the family of A G protein-coupled receptors that comprises responses to hormones and neurotransmitters. This affects, among other things, reproduction, development, locomotion, and feeding. Furthermore, amitraz and its metabolites were predicted as active compounds interacting with diverse receptors of the Tox21-nuclear receptor signaling and stress response pathways.

Degradation mechanism and eco-toxicity assessment of bisphenol S based on peroxymonosulfate activated with Co3O4 surfaces

In this paper, the degradation mechanism and eco-toxicity assessment of bisphenol S (BPS) was studied in the aqueous environment based on the heterogeneous activation of peroxymonosulfate (PMS) through spinel tricobalt tetraoxide (Co3O4) catalyst using density functional theory (DFT) and computational toxicology methods. The result indicates that (100) and (311) surfaces have the higher proportion of bivalent cobalt, the increase of charge transfer, negative adsorption energy, and the lower surface energy. They can more effectively promote the decomposition of PMS to generate reactive oxide species. When generated oxidants react with BPS, HO•-addition reaction at the ortho-C atom-sites plays a dominant role in the system Co3O4/PMS. The presence of SO4•-, HO•, and O2 promotes the formation of transformation intermediates and products. The formation pathways of important experimental intermediates hydroquinone, p-hydroxybenzenesulfonic acid, and 3, 4-dihydroxybenzenesulfonic acid are identified. And, some hydroxylation products that are not identified in the experiment are determined. The eco-toxicity evaluation shows that most of the decomposition products are completely harmless or significantly reduced compared to BPS. This study not only provides insights into the degradation mechanism of BPS in Co3O4/PMS system, but also is expected to be a guide for further experimental research and the design and optimization of the activated catalysts in sulfate radical-based advanced oxidation technologies.

Increasing the acceptance of in silico toxicology through development of protocols and position papers

In silico toxicology protocols are currently needed to support the acceptance and deployment of computational toxicology methods as alternative methods for health hazard identification. Such protocols combine relevant in silico results with available experimental data to derive an assessment of major toxicological endpoints supported by a confidence score reflecting the uncertainty in the assessment. The protocols also identify relevant effects and/or mechanisms which can be used to guide the assessment of a toxicological endpoint. In addition, sufficient documentation of procedures and methods used to support an assessment is essential for both internal and external decision-making. The combination of relevant data, confidence scoring, and reporting provides a hazard assessment framework intended to increase the acceptance of in silico results in a toxicologic assessment. This article describes key principles and components of such protocols, including the hazard assessment framework and recommendations demonstrating how evaluating relevance, completeness, and confidence can be performed and documented. Also discussed are criteria used to develop an in silico protocol based on the state of the science and the importance of developing position papers to outline roadmaps for future in silico protocols used to guide assessments of more complex toxicological endpoints, such as cancer or neurotoxicity. The current status of providing such protocols is summarized for specific in silico protocols that are already published, in development, or planned.

Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training

Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure–activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.

Intelligent testing strategy and analytical techniques for the safety assessment of nanomaterials.

Nanomaterials (NMs) are widely used in various areas because of their unique and useful physicochemical properties. However, they may pose toxicity risks to human health after exposure. Applicable and reliable approaches are needed for risk assessment of NMs. Herein, an intelligent analytical strategy for safety assessment of NMs is proposed that focuses on toxicity assessment using an in vitro cell model. The toxicity assessment by testing on the adverse outcome pathway in a cell culture system was defined by application of a tiered testing approach. To provide an overview of the applicable approach for risk assessment of NMs, we discuss the most commonly used techniques and analytical methods, including computational toxicology methods in dosimetry assessment, high-throughput screening for toxicity testing with high efficiency, and omics-based toxicology assessment methods. The final section focuses on the route map for an integrated approach to a testing and assessment strategy on how to extrapolate the in vitro NM toxicity testing data to in vivo risk assessment of NMs. The intelligent analytical strategy, having evolved step-by-step, could contribute to better applications for safety evaluation and risk assessment of NMs in reality.

Computational Toxicology and Drug Discovery.

The use of computational toxicology methods within drug discovery began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been continuously expanding ever since and the tasks at hand have become more complex. These approaches are now strategically integrated into the risk assessment process, as a complement to in vitro and in vivo methods. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life-cycle management. This chapter provides an overview of the field and describes the application of computational toxicology throughout the entire discovery and development process.

In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data.

One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds' features may improve model's performance and reduce the number of required models. This can be achieved through multi‐label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi‐label classification models generated using combinations of the state‐of‐the‐art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.This article is categorized under: 1Computer and Information Science > Chemoinformatics2Computer and Information Science > Computer Algorithms and Programming

Integrated strategy for mutagenicity prediction applied to food contact chemicals.

Food contamination due to unintentional leakage of chemicals from food contact materials (FCM) is a source of increasing concern. Since for many of these substances, only limited or no toxicological data are available, the development of alternative methodologies to establish rapidly and cost-efficiently level of safety concern is critical to ensure adequate consumer protection. Computational toxicology methods are considered the most promising solutions to cope with this data gap. In particular, mutagenicity assessment has a particular relevance and is a mandatory requirement for all substances released from plastic FCM, regardless how low migration and exposure are. In the present work, a strategy integrating a number of (Quantitative) Structure Activity Relationship ((Q)SAR) models for Ames mutagenicity predictions is proposed. A list of chemicals representing likely migrating moieties from FCM was selected to test the value of the newly defined strategy and the possibility to combine predictions given by the different algorithms was evaluated. In particular, a scheme to integrate mutagenicity estimations into a single final assessment was developed resulting in an increased domain of applicability. In most cases, a deeper analysis of experimental data, where available, allowed fixing misclassification errors, highlighting the importance of data curation in the development, validation and application of in silico methods. The high accuracy of the strategy provided the rationales for its application for toxicologically uncharacterized chemicals. Finally, the overall strategy of integration will be automated through its implementation into a freely available software application.

Reliable predictive computational toxicology methods for mixture toxicity: toward the development of innovative integrated models for environmental risk assessment

A main objective in the field of mixture toxicity is to determine how well combined effects are predictable based on the known effects of mixture constituents. Conducting toxicity tests for all conceivable combinations of chemicals, to understand all mechanisms in the combined toxicity of environmental pollutants, is virtually unfeasible due to cost- and time-consuming procedures. Therefore, predictive tools for mixture toxicity are required to be developed within the applicable range of predictive toxicology. The concept of concentration addition (CA) model is often considered a general method for estimating mixture toxicity at the regulatory level. In the long run, however, the possibility of toxicological synergism between mixture components actually occurs, especially from the no-effect level or non-toxic substances. This is ignored under the CA concept, and needs to be examined and integrated into existing addition models at a scientific level, this paper reviews existing integrated models for estimating the toxicity of complex mixtures in literature. Current approaches to assess mixture toxicity and the need for new research concepts to overcome challenges which recent studies have confronted are discussed, particularly those involved in computational approaches to predict mixture toxicity in an environment risk assessment based on mixture components.

A discussion of the impact of us chemical regulation legislation on the field of toxicity testing

Proposals for revising the principal United States law governing industrial chemicals, the Toxic Substances Control Act, have been under consideration in the US Congress for the past several years, and some version of such legislation may be passed in the near future. Concurrently, a desire to move away from current testing methods for ethical, scientific, and practical reasons has led to multi-million dollar investments in in vitro and computational toxicology methods and programs. Legislative language has the potential to endorse this transition and facilitate its fruition, or conversely enshrine in vivo testing methods for the foreseeable future. New legislation also has the potential to substantially increase the numbers of animals used in toxicity tests in the near term. However, there are a number of policies that, used effectively, can reduce the overall number of animals used in new toxicity tests. We present recent legislative proposals in the context of current testing programs and discuss their potential impacts on animal use, test method innovation, and achievement of desired legislative objectives. Discussions like these are essential to judiciously select policies that reduce the use of animals in toxicity testing and protect human health and the environment.

Computational Toxicology Approaches at the US Food and Drug Administration

For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA's Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA's efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products - however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the "black box" paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.

Toxicity assessment of unintentional exposure to multiple chemicals

Typically exposure to environmental chemicals is unintentional, and often the exposure is to chemical mixtures, either simultaneously or sequentially. When exposure occurs, in public health practice, it is prudent to ascertain if thresholds for harmful health effects are exceeded, whether by individual chemicals or by chemicals in combination. Three alternative approaches are available for assessing the toxicity of chemical mixtures. Each approach, however, has shortcomings. As the procedures of each approach are described in this paper, at various steps research needs are identified. Recently, reliance has increased on computational toxicology methods for predicting toxicological effects when data are limited. Advances in molecular biology, identification of biomarkers, and availability of accurate and sensitive methods allow us to more precisely define the relationships between multiple chemical exposures and health effects, both qualitatively and quantitatively. Key research needs are best fulfilled through collaborative research. It is through such collaborations that resources are most effectively leveraged to further develop and apply toxicity assessment methods that advance public health practices in vulnerable communities.

Application of physiologically-based pharmacokinetic modeling to investigate the toxicological interaction between chlorpyrifos and parathion in the rat

Environmental exposure is usually due to the presence of multiple chemicals. In most cases, these chemicals interact with each other at both pharmacokinetic and pharmacodynamic toxicity mechanisms. In the absence of data, joint toxicity assessment of a mixture is based on default dose or response additivity. Although, the concept of additivity is mostly accepted at low dose levels, these levels need to be determined quantitatively to validate the use of additivity as an absence of any possible synergistic or antagonistic interactions at low environmental exposure levels. The doses at which interaction becomes significant define the interaction threshold. In most cases, estimation of these low-dose interaction thresholds experimentally is economically costly and challenging because of the need to use a large number of laboratory animals. Computational toxicology methods provide a feasible alternative to establish interaction thresholds. For example, a physiologically based pharmacokinetic (PBPK) model was developed to estimate an interaction threshold for the joint toxicity between chlorpyrifos and parathion in the rat. Initially, PBPK models were developed for each chemical to estimate the blood concentrations of their respective metabolite. The metabolite concentrations in blood out-put was then linked to acetylcholinesterase kinetics submodel. The resulting overall PBPK model described interactions between these pesticides at two levels in the organism: (a) the P450 enzymatic bioactivation site, and (b) acetylcholinesterase binding sites. Using the overall model, a response surface was constructed at various dose levels of each chemical to investigate the mechanism of interaction and to calculate interaction threshold doses. The overall model simulations indicated that additivity is obtained at oral dose levels below 0.08 mg/kg of each chemical. At higher doses, antagonism by enzymatic competitive inhibition is the mode of interaction.

Applications of computational toxicology methods at the Agency for Toxic Substances and Disease Registry

AbstractIn its efforts to provide consultations to state and local health departments, other federal agencies, health professionals, and the public on the health effects of environmental pollutants, the Agency for Toxic Substances and Disease Registry relies on the latest advances in computational toxicology. The computational toxicology laboratory at the agency is continually engaged in developing and applying models for decision-support tools such as physiologically based pharmacokinetic (PBPK) models, benchmark dose (BMD) models, and quantitative structure-activity relationship (QSAR) models. PBPK models are suitable for connecting exposure scenarios to biological indicators such as tissue dose or end point response. The models are used by the agency to identify the significance of exposure routes in producing tissue levels of possible contaminants for people living near hazardous waste sites. Additionally, PBPK models provide a credible scientific methodology for route-to-route extrapolations of health guidance values, which are usually determined from a very specific set of experiments. Also, scientists at the computational toxicology laboratory are using PBPK models for advancing toxicology research in such areas as joint toxicity assessment and child-based toxicity assessments. With BMD modeling, all the information embedded in an experimentally determined dose-response relationship is used to estimate, with minimum extrapolations, human health guidance values for environmental substances. Scientists in the laboratory also rely on QSAR models in the many cases where consultations from the agency are reported for chemicals that lack adequate experimental documentation.